Chapter 7 - Control Flow, Loops and Functions

Author

Government Analysis Function and ONS Data Science Campus

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1 Introduction

We use R because it’s great for data analysis, data visualisation communicating results. However, R is not just a data analysis environment but a programming language. Advanced R programmers can develop complex packages and even improve R itself.

In this chapter, we introduce three key programming concepts: loops, conditional statements, and functions. These are not just key building blocks for advanced programming, but are sometimes useful during data analysis.

1.1 Learning Outcomes

By the end of this chapter you should be able to:

  • Understand the programming concept of loops.
  • Use loops and understand their place in R.
  • Be able to use conditional statements: if, else and else if.
  • Be familiar with else_if() and case_when() from dplyr.
  • Know what functions are and why they are useful.
  • Be able to distinguish between a function’s parameters and arguments.
    • Including different types of arguments
  • Understand the idea of ‘scope’.
    • i.e. Global scope vs Local scope
  • Be able to write and apply user defined functions.
  • Be able to apply functions to vector and tibble objects.

The chapter assumes familiarity with:

  • Logical Operators such as less than (<) , greater than (>), equivalent to (==) etc
  • Knowledge of reading in data (using readr); basic data manipulation (using dplyr)
  • General basic knowledge of R code - this course should not be your first introduction to the language.

2 Packages and Datasets

2.1 Packages

Below find listed the packages used in this course. This course has been tested with the versions listed.

This course uses various packages from the Tidyverse collection of packages. These can be loaded individually or as a whole through library(tidyverse)

  • readr - version 1.4.0
  • dplyr - version 1.0.6
  • purr - version 0.3.4

Other Packages

  • janitor - version 2.1.0
  • fs - 1.5.0
library(tidyverse)
Warning: package 'tidyverse' was built under R version 4.4.2
Warning: package 'ggplot2' was built under R version 4.4.2
Warning: package 'tibble' was built under R version 4.4.2
Warning: package 'tidyr' was built under R version 4.4.2
Warning: package 'readr' was built under R version 4.4.2
Warning: package 'purrr' was built under R version 4.4.2
Warning: package 'dplyr' was built under R version 4.4.2
Warning: package 'stringr' was built under R version 4.4.2
Warning: package 'forcats' was built under R version 4.4.2
Warning: package 'lubridate' was built under R version 4.4.2
── 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 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::group_rows() masks kableExtra::group_rows()
✖ dplyr::lag()        masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(janitor)

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test
library(fs)
Warning: package 'fs' was built under R version 4.4.2

2.2 Data

In this course we will use the data titanic_clean.csv and assign it to titanic

In this file missing values have been specified and headers have been cleaned already for you.

titanic <- readr::read_csv("Data/titanic_clean.csv")
Rows: 1309 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): name_of_passenger, sex_of_passenger, ticket, cabin, embarked
dbl (6): pclass, survived, age_of_passenger, sibsp, parch, fare

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

To Inspect the data we can use:

dplyr::glimpse(titanic)
Rows: 1,309
Columns: 11
$ pclass            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ survived          <dbl> 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1…
$ name_of_passenger <chr> "Allen, Miss. Elisabeth Walton", "Allison, Master. H…
$ sex_of_passenger  <chr> "female", "male", "female", "male", "female", "male"…
$ age_of_passenger  <dbl> 29.0000, 0.9167, 2.0000, 30.0000, 25.0000, 48.0000, …
$ sibsp             <dbl> 0, 1, 1, 1, 1, 0, 1, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0…
$ parch             <dbl> 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…
$ ticket            <chr> "24160", "113781", "113781", "113781", "113781", "19…
$ fare              <dbl> 211.3375, 151.5500, 151.5500, 151.5500, 151.5500, 26…
$ cabin             <chr> "B5", "C22 C26", "C22 C26", "C22 C26", "C22 C26", "E…
$ embarked          <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "C", "C…


We can see these these are columns,

  • Pclass: Passenger’s class, 1 = 1st (Upper), 2 = 2nd(Middle), 3 = 3rd(Lower)
  • Survived: Survived (1) or died (0), 0 = No, 1 = Yes
  • Name: Passenger’s name
  • Sex: Passenger’s sex
  • Age: Passenger’s age
  • SibSp: Number of siblings/spouses aboard
  • Parch: Number of parents/children aboard
  • Ticket: Ticket number
  • Fare: Fare
  • Cabin: Cabin number
  • Embarked: Port of embarkation, C = Cherbourg, Q = Queenstown, S = Southampton

We can see more details on the Data Dictionary

3 Loops

Loops are a fundamental concept in traditional programming languages.

A loop is a way to repeat a number of commands until a given condition is met.

This repetition of code is called iteration.

A repetitive action could be “create several similar plots”.

Within a loop, any other code can be run to produce anything we want such as plots, models, reports, and datasets.

Since R is a vectorised language, loops are not as prominently used in R as in Python or other programming languages. Despite this, they are a key part of programming in general and can save you a large amount of time and typing in certain cases.

They allow you to automate parts of your code that are in need of repetition.

Similar to how functions help make our code more abstract and general, loops perform a similar purpose. We are essentially simplifying a specific case of code to a more general case.

We are going to look at for loops first.

3.1 For Loops

These are the most common type of loop the other type is a while loop which can do the same things, but in a slightly different way.

For loops follow the basic structure below.

# Basic Structure of a loop

# Creating the loop
for (each_item in my_iterable) { 
  
     output <- commands
  }
  • We start the loop with the word for,

  • Followed by () brackets where we first specify an index variable, i is commonly used but it can be anything that you want. Remember, we want to use clear and descriptive variable names. This is a place holder and corresponds to each different element as we move through the loop,

  • Then the word in.

  • Then specify an iterable. This could be a vector, a list, a dataframe etc. An iterable is any object that can be iterated through, one element at a time.

  • Followed by the {} curly brackets, which will have our commands within, these could be multiple lines of code.

3.1.1 Example

Let’s look at an example.

We start by creating an iterable, I have created a vector with the numbers 0 through 5.

# Creating a vector

example_vector <- c(0, 1, 2, 3, 4, 5)

We can then create our loop which doubles every value and prints it out.

# Creating the loop

for (each_number in example_vector) {
     
     # Print the value at each step
     print(each_number * 2)
  
  }
[1] 0
[1] 2
[1] 4
[1] 6
[1] 8
[1] 10

Basic for loop as detailed in the previous exercise

In the above example we simply printed our results in the console, if we wanted to store our result, we can simply create a data structure of our choice and use the append function, as shown below.

The keyword for is followed by a variable that refers to each item in my iterable. I’ve called this variable each_number. It is good to be clear and explicit when naming variables, so they explain what the variable is.

The keyword in is followed by the iterable I want to loop over - example_vector.

The output of my command is appended to result. The command here is to multiply each number in example_vector by 2. The append() function simply adds an element to the end of a vector. We first specify the vector we want to add to which is result and then specify the values that we want to add to it. Within the function this is set to the parameter values.

# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Creating the loop

for (each_number in example_vector) {
  
     # Print the value at each step
     print(each_number * 2)
  
     result <- append(result, values = each_number * 2)
  
}
[1] 0
[1] 2
[1] 4
[1] 6
[1] 8
[1] 10
# To display the data

result
[1]  0  2  4  6  8 10


Although it is useful to know how to use loops in R, it is often faster to accomplish the same thing using vectorised operations in R.

We already saw examples in the Vector Arithmetic section. A vectorised function is a function that will apply the same operation on each element of the vectors.

For example:

# Creating a vector

example_vector <- 1:5

# Using a sqrt() function on our vector

sqrt(example_vector)
[1] 1.000000 1.414214 1.732051 2.000000 2.236068


To make this calculation, there is no need for a loop. However, not all functions work this way.

While there are many functions out there that vectorise particular calculations, there are still some tasks that cannot be vectorised. This is where iteration becomes useful. While we should always be looking to vectorise calculations, we can take comfort knowing that we have a back up tool to use just in case it fails us.

We also have while loops, which are used to loop until a specific condition is met.

You can a find a tutorial on them here: While Loop.

3.1.2 Exercise 1

  1. You are given a vector of measurements that are grams (g).

    For this exercise, we want to convert all of these measurements to kilograms (kg).

    For reference, 1kg is equal to 1000 grams.

    Using a for loop, create and append to a new vector called kilograms that contains the values converted to kilograms.

# Vector 

grams <- c(100000, 7899900, 967312, 49185, 6100)

Please note below is pseudocode, that is the ‘recipie’ for getting the answer.

# Starting vector

grams <- c(100000, 7899900, 967312, 49185, 6100)

# Empty vector for result storage

kilograms <- c()

# For loop

for (each_number in vector) {
  
     convert to kilograms
  
     append result to kilograms
}


# To display the data

kilograms
# Starting vector

grams <- c(100000, 7899900, 967312, 49185, 6100)

# Empty vector for result storage

kilograms <- c()

# For loop

for (measurement in grams) {
    # Converting grams to kg
    converted_kilogram <- measurement / 1000 
  
    # Appending to kilo_grams result vector
    kilograms <- append(kilograms, values = converted_kilogram) 
  
}

# To display the data

kilograms
[1]  100.000 7899.900  967.312   49.185    6.100

3.1.3 Extension Exercise

If you would like to go deeper into for loops before moving on, try the extension exercise below.

  1. Complete the code below, Use a for loop to load all the files in the data folder. Some steps have been given to you already.
  • We have loaded the packages tidyverse and fs

  • We then use the function fs::dir_ls() which will give a list of all the file paths in the data folder, we have assgined this to file paths.

  • We have created an empty list called my_datasets which we fill with out datasets after the loop runs.

  • Your task is to create a for loop to loop through the file_paths and store each dataset as an element in the list my_datasets

# Loading packages

library(tidyverse) # For loading data and manipulation of data
library(fs) # File system


# Get a list of the file paths
file_paths <- fs::dir_ls("Data") 

# Display the file paths
file_paths
Data/bikes_data_set.csv   Data/imdb_scores.csv      Data/netflix_data.csv     
Data/police_data.xlsx     Data/revenue_data_set.csv Data/titanic.csv          
Data/titanic_clean.csv    
# Create a list to store all the dataframes
my_datasets <- list()
# Loading packages

library(tidyverse) # For loading data and manipulation of data
library(fs) # File system


# Get a list of the file paths
file_paths <- fs::dir_ls("Data") 

# Display the file paths
file_paths

# Create a list to store all the dataframes
my_datasets <- list()


# Loop through file paths and store in the list

for (each_file_path in file_paths){
  
     # adding a new element in the my datasets list
     my_datasets <- command 
  }
library(tidyverse) # For loading data and manipulation of data
library(fs) # File system


# Get a list of the file paths
file_paths <- fs::dir_ls("Data") 

# Display the file paths
file_paths
Data/bikes_data_set.csv   Data/imdb_scores.csv      Data/netflix_data.csv     
Data/police_data.xlsx     Data/revenue_data_set.csv Data/titanic.csv          
Data/titanic_clean.csv    
# Create a list to store all the dataframes
my_datasets <- list()

# Loop through file paths and store in the list

for (each_file_path in file_paths){
  
     # adding a new element in the my datasets list
     my_datasets[[each_file_path]] <- readr::read_csv(file = file_paths[[each_file_path]])
}

# To display the list of dataframes
dplyr::glimpse(my_datasets)
List of 7
 $ Data/bikes_data_set.csv  : spc_tbl_ [737 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ Date                    : chr [1:737] "04/01/2015" "05/01/2015" "04/01/2015" "05/01/2015" ...
  ..$ real Temperature        : num [1:737] 2.75 9 2.75 9 NA NA 8 9 9 9 ...
  ..$ empty column            : logi [1:737] NA NA NA NA NA NA ...
  ..$ Feel Temperature        : num [1:737] 0 7.25 0 7.25 NA NA 5.75 5.5 6.75 6.75 ...
  ..$ Humidity                : num [1:737] 93 81.5 93 81.5 NA ...
  ..$ Wind Speed              : num [1:737] 7.5 8.85 7.5 8.85 NA ...
  ..$ weather code            : chr [1:737] "broken_clouds" "broken_clouds" "broken_clouds" "broken_clouds" ...
  ..$ DO NOT TOUCH THIS COLUMN: logi [1:737] NA NA NA NA NA NA ...
  ..$ is holiday              : logi [1:737] FALSE FALSE FALSE FALSE NA NA ...
  ..$ is weekend              : logi [1:737] TRUE FALSE TRUE FALSE NA NA ...
  ..$ season                  : chr [1:737] "winter" "winter" "winter" "winter" ...
  ..$ count                   : num [1:737] 9234 20372 9234 20372 NA ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   Date = col_character(),
  .. ..   `real Temperature` = col_double(),
  .. ..   `empty column` = col_logical(),
  .. ..   `Feel Temperature` = col_double(),
  .. ..   Humidity = col_double(),
  .. ..   `Wind Speed` = col_double(),
  .. ..   `weather code` = col_character(),
  .. ..   `DO NOT TOUCH THIS COLUMN` = col_logical(),
  .. ..   `is holiday` = col_logical(),
  .. ..   `is weekend` = col_logical(),
  .. ..   season = col_character(),
  .. ..   count = col_double()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/imdb_scores.csv     : spc_tbl_ [7,787 × 3] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ title         : chr [1:7787] "Aerials" "Justin Bieber: Never Say Never" "5Gang" "Kyaa Kool Hain Hum 3" ...
  ..$ average Rating: num [1:7787] 1.5 1.6 1.8 1.9 2.1 2.1 2.2 2.2 2.4 2.5 ...
  ..$ num Votes     : num [1:7787] 328 75891 2587 4217 439 ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   title = col_character(),
  .. ..   `average Rating` = col_double(),
  .. ..   `num Votes` = col_double()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/netflix_data.csv    : spc_tbl_ [7,787 × 17] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ show_id        : chr [1:7787] "s1" "s2" "s3" "s4" ...
  ..$ type           : chr [1:7787] "TV Show" "Movie" "Movie" "Movie" ...
  ..$ title          : chr [1:7787] "3%" "07:19" "23:59" "9" ...
  ..$ director       : chr [1:7787] NA "Jorge Michel Grau" "Gilbert Chan" "Shane Acker" ...
  ..$ cast           : chr [1:7787] "João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Me"| __truncated__ "Demián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen Beato" "Tedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin Lim" "Elijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasc"| __truncated__ ...
  ..$ primary_country: chr [1:7787] "Brazil" "Mexico" "Singapore" "United States" ...
  ..$ country_2      : chr [1:7787] NA NA NA NA ...
  ..$ country_3      : chr [1:7787] NA NA NA NA ...
  ..$ country_4      : chr [1:7787] NA NA NA NA ...
  ..$ countries_5_up : chr [1:7787] NA NA NA NA ...
  ..$ date_added     : chr [1:7787] "August 14, 2020" "December 23, 2016" "December 20, 2018" "November 16, 2017" ...
  ..$ release_year   : num [1:7787] 2020 2016 2011 2009 2008 ...
  ..$ rating         : chr [1:7787] "TV-MA" "TV-MA" "R" "PG-13" ...
  ..$ duration       : chr [1:7787] "4 Seasons" "93 min" "78 min" "80 min" ...
  ..$ listed_in      : chr [1:7787] "International TV Shows, TV Dramas, TV Sci-Fi & Fantasy" "Dramas, International Movies" "Horror Movies, International Movies" "Action & Adventure, Independent Movies, Sci-Fi & Fantasy" ...
  ..$ description    : chr [1:7787] "In a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join t"| __truncated__ "After a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued wh"| __truncated__ "When an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunt"| __truncated__ "In a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until"| __truncated__ ...
  ..$ num_cast       : num [1:7787] 11 6 9 9 12 10 8 5 9 8 ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   show_id = col_character(),
  .. ..   type = col_character(),
  .. ..   title = col_character(),
  .. ..   director = col_character(),
  .. ..   cast = col_character(),
  .. ..   primary_country = col_character(),
  .. ..   country_2 = col_character(),
  .. ..   country_3 = col_character(),
  .. ..   country_4 = col_character(),
  .. ..   countries_5_up = col_character(),
  .. ..   date_added = col_character(),
  .. ..   release_year = col_double(),
  .. ..   rating = col_character(),
  .. ..   duration = col_character(),
  .. ..   listed_in = col_character(),
  .. ..   description = col_character(),
  .. ..   num_cast = col_double()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/police_data.xlsx    : spc_tbl_ [1 × 1] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ <?xml version="1.0" encoding="UTF-8" standalone="yes"?>: chr "<Types xmlns=\"http://schemas.openxmlformats.org/package/2006/content-types\"><Default Extension=\"bin\" Conten"| __truncated__
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   `<?xml version="1.0" encoding="UTF-8" standalone="yes"?>` = col_character()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/revenue_data_set.csv: spc_tbl_ [20 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ year         : num [1:20] 1999 2000 2001 2002 2003 ...
  ..$ quarter_one  : num [1:20] 0 0.9 0.7 0.2 0.6 0.4 0.2 0.1 0.1 0.7 ...
  ..$ quarter_two  : num [1:20] 0.6 0.2 0.7 0.6 0 0.1 0 0.2 0.9 0.6 ...
  ..$ quarter_three: num [1:20] 0.5 0.9 0.5 1 0.1 0.5 0.1 0.5 0.3 0.5 ...
  ..$ quarter_four : num [1:20] 0.2 0.5 0.4 0.8 1 0 0.4 0.4 0.8 0.7 ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   year = col_double(),
  .. ..   quarter_one = col_double(),
  .. ..   quarter_two = col_double(),
  .. ..   quarter_three = col_double(),
  .. ..   quarter_four = col_double()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/titanic.csv         : spc_tbl_ [1,309 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ Pclass           : num [1:1309] 1 1 1 1 1 1 1 1 1 1 ...
  ..$ Survived         : num [1:1309] 1 1 0 0 0 1 1 0 1 0 ...
  ..$ name Of Passenger: chr [1:1309] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
  ..$ Sex of passenger : chr [1:1309] "female" "male" "female" "male" ...
  ..$ Age of passenger : chr [1:1309] "29" "0.9167" "2" "30" ...
  ..$ Sibsp            : num [1:1309] 0 1 1 1 1 0 1 0 2 0 ...
  ..$ Parch            : num [1:1309] 0 2 2 2 2 0 0 0 0 0 ...
  ..$ Ticket           : chr [1:1309] "24160" "113781" "113781" "113781" ...
  ..$ Fare             : num [1:1309] 211 152 152 152 152 ...
  ..$ Cabin            : chr [1:1309] "B5" "C22 C26" "C22 C26" "C22 C26" ...
  ..$ Embarked         : chr [1:1309] "S" "S" "S" "S" ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   Pclass = col_double(),
  .. ..   Survived = col_double(),
  .. ..   `name Of Passenger` = col_character(),
  .. ..   `Sex of passenger` = col_character(),
  .. ..   `Age of passenger` = col_character(),
  .. ..   Sibsp = col_double(),
  .. ..   Parch = col_double(),
  .. ..   Ticket = col_character(),
  .. ..   Fare = col_double(),
  .. ..   Cabin = col_character(),
  .. ..   Embarked = col_character()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 
 $ Data/titanic_clean.csv   : spc_tbl_ [1,309 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
  ..$ pclass           : num [1:1309] 1 1 1 1 1 1 1 1 1 1 ...
  ..$ survived         : num [1:1309] 1 1 0 0 0 1 1 0 1 0 ...
  ..$ name_of_passenger: chr [1:1309] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
  ..$ sex_of_passenger : chr [1:1309] "female" "male" "female" "male" ...
  ..$ age_of_passenger : num [1:1309] 29 0.917 2 30 25 ...
  ..$ sibsp            : num [1:1309] 0 1 1 1 1 0 1 0 2 0 ...
  ..$ parch            : num [1:1309] 0 2 2 2 2 0 0 0 0 0 ...
  ..$ ticket           : chr [1:1309] "24160" "113781" "113781" "113781" ...
  ..$ fare             : num [1:1309] 211 152 152 152 152 ...
  ..$ cabin            : chr [1:1309] "B5" "C22 C26" "C22 C26" "C22 C26" ...
  ..$ embarked         : chr [1:1309] "S" "S" "S" "S" ...
  ..- attr(*, "spec")=
  .. .. cols(
  .. ..   pclass = col_double(),
  .. ..   survived = col_double(),
  .. ..   name_of_passenger = col_character(),
  .. ..   sex_of_passenger = col_character(),
  .. ..   age_of_passenger = col_double(),
  .. ..   sibsp = col_double(),
  .. ..   parch = col_double(),
  .. ..   ticket = col_character(),
  .. ..   fare = col_double(),
  .. ..   cabin = col_character(),
  .. ..   embarked = col_character()
  .. .. )
  ..- attr(*, "problems")=<externalptr> 

If you want the datasets to be displayed as separate datasets in the environment window, you can use the list2env(), here we specify the list my_datasets and the argument envir=.GlobalEnv.

# To display in environment as separate datasets
list2env(my_datasets,envir=.GlobalEnv)
<environment: R_GlobalEnv>

Like we said lists are the not the only way to do this, other options would be to use the lapply() or the purrr::map() function.

To use either of these functions all we need is to provide the file path list and then the function we want to run on the list.

Please note that when we use the function inside lapply or map we don’t include the brackets as we are not passing any inputs into the function, hence we read_csv instead of read_csv(), so lapply/map is the function and read_csv is like an input/argument

# Using lapply to load multiple files
my_datasets <- lapply(file_paths, read_csv)
 
 
# Using lapply to load multiple files
my_datasets <- purrr::map(file_paths, read_csv)

As you can see that we can we achieve the same result as the loop.

3.2 While loops

Our other type of loops is the while loop.

for loops iterate a set amount of times, or over a set number of things. A while loop iterates until it no longer meets a certain condition.

# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Creating the loop

while (logical_condition) { 
  
  result <- commands
  
}

# To display the data

result
  • So we start with creating an empty vector for our output

  • Then the word while

  • followed by the () brackets in which we specify our logical condition, using the conditional operators.

  • and then {}, which surrounds our commands. These commands will be run repeatedly in order as long as the specified condition is TRUE.

3.2.1 Example

Let’s look at an example:

  • Let us first set the stop_value to 0.

  • and we set our condition to be stop_value < 5

  • then print out the value of stop_value

  • and then add one to the value.

# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Setting the value

stop_value <- 0

# Creating the loop
# Our condition here is that stop_value < 5

while (stop_value < 5) { 
  
  print(stop_value)
  
  stop_value <- stop_value + 1 
  
  result <-  append(result, stop_value + 1)
}
[1] 0
[1] 1
[1] 2
[1] 3
[1] 4
# To display the data

result
[1] 2 3 4 5 6

Can you guess what would happen if we removed the stop_value <- stop_value + 1 ?

# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Setting the value

stop_value <- 0

# Creating the loop
# Our condition here is that 0<5 i.e stop_value

while (stop_value < 5) { 
  
  print(stop_value)
  
  result <- append(result, values = stop_value + 1)
}

# To display the data

result

If we removed the code stop_value <- stop_value +1 , stop_value (0) would always be less than 5 and the loop would be stuck in an infinite loop, as R will never stop running the code!

You will have to hit the stop sign to exit the loop!

It is therefore important that we design while loops that have a logical condition that will be met at some point.

Although it is useful to know how to use loops in R, it is often faster to accomplish the same thing using vectorised operations in R.

We already saw examples in the Vector Arithmetic section. A vectorized function is a function that will apply the same operation on each of the vectors.

For example,

# Creating a vector

example_vector <- 1:5

# Using a sqrt() function on our vector

sqrt(example_vector)
[1] 1.000000 1.414214 1.732051 2.000000 2.236068

To make this calculation, there is no need for a loop. However, not all functions work this way.

3.3 Exercise

  1. Code a while loop with the following characteristics:
  • Assign the value of a variable temperature to be 40.

  • The condition of the while loop should check if the temperature is higher than 20

  • The first command in the body should print out "Its way too hot!"

  • The next command should decrease the temperature by 2 and assign this new value to temperature again.

# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Setting the temperature value

temperature <- 40

# Code the while loop

while (condition) {
  
  conditions
  
  result <- append(result, temperature)
  
}

# To display the data

result
# Create an empty output
# Which we will fill when the loop runs

result <- c()

# Setting the temperature value

temperature <- 40

# Code the while loop

while (temperature > 20) {
  
  print("It way too hot!")
  
  temperature <- temperature - 2
  
  result <- append(result, temperature)
  
}
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
[1] "It way too hot!"
# To display the data

result
 [1] 38 36 34 32 30 28 26 24 22 20

4 Conditional Statements

4.1 If, else if, else

A conditional statement will check whether a statement is TRUE or FALSE.

If a criteria is met, we may want to run different code than if it is not met.

This is useful if we want a function or code block to run only when our data meets certain conditions; this is termed control flow.

Control flow is something we do in everyday life.

For example when we go shopping and choose fruits, we normally want to buy the ones that look juicy and healthy and avoid the ones that look rotten or bruised.

As we pick up our fruit and inspect it, we are actually using control flow.

Conditional statements checking whether a fruit is suitable to purchase

In order to move through this decision process, we need to answer either “Yes” or “No” to each question.

A series of “Yes” answers will lead us to buy an orange that matches our set criteria.

This links in with the logical or Boolean data types that we covered in Chapter One, Getting Started With R, as our logical statement has to be either TRUE or FALSE.

To evaluate these we use comparison operators which we used in Chapter 4 Working With Dataframes where we used the filter() function.

These comparison and logical operators allow us to control the flow of our code.

Comparison Operators.

Operator Description
== equal to
!= not equal to
< less than
> greater than
<= less than or equal to
>= greater than or equal to

Logical Operators.

Operator Description
& And
| Or
! Not
Condition 1 Condition 2 & (AND) Equates to | (OR) Equates to
True True True True
True False False True
False True False True
False False False False

Our example above shows that we can make a decision by answering “Yes” or “No” at two key points. But often this will not be the case - we might have only a single question, or we might want to add some more branches to our responses.

R allows us to have complete control over our statement by using the if, else if, and else keywords.

We always first put an:

  • if statement

then an optional number (0 to many) of:

  • else if statements

then finally an optional singular:

  • else statement

if, else if and else are not loops, but fall under the category of control flow.

These take a conditional argument, and if that is met (evaluates to TRUE) then the associated action is completed.

If the condition is not met it will move to the next statement and try to evaluate it; if that condition is met the associated action will be completed.

Finally, if none of the conditions are met, the else condition’s associated actions are run.

We separate out out different command blocks relating to each condition using curly brackets {} around the corresponding block of code.

if (first_condition) { 
  
    execute_this
  
} else if (second_condition) { 
  
    execute_this
  
} else {
  
    execute_this
  
}

It’s important that the else if keyword comes on the same line as the closing bracket of the previous part of the control structure.

Simple if, else if, and else diagram

Let’s look at an example.

We can just have an if and an else - else if statements are optional (as is the final else statement!)

height <- 5

if (height == 5) { 
  
    print("We're the same height")
  
} else {
  
    print("Your height is different")
  
}
[1] "We're the same height"

Remember our if, else if and else statements take a condition so we need a double equals sign == for checking equivalence.

If the first criteria isn’t met it will move to the else statement and action that.

We can add in more choices using an else if statement.

height <- 7

if (height == 5) { 
  
    print("Your height is 5")
  
} else if (height == 7) { 
  
    print("We are the same height")
 
} else {
  
   print("Your height is not 5 or 7")
  
}
[1] "We are the same height"

Within our condition we can use the logical operators too: & (and); | (or), in order to make more complex conditions.

Here we want the action to happen if the value is greater than 7 and less than 10.

height <- 9

if (height == 5) { 
  
   print("Your height is 5")
  
} else if (height > 7 & height < 10) { 
  
   print("You're height is between 7 and 10")
  
} else {
  
   print("Your height is different")
  
}
[1] "You're height is between 7 and 10"

It’s important to know how the if, else if, else works.

Below we have two options for when the value is 5. However it will only print out “We’re the same height!” as when a condition is met, the code associated with it is run, and R stops evaluating the rest of the conditionals.

In this code the block print("Wow, it's still 5!") will never run. If the height is equal to 5, the first if condition will run, and then the logic stops.

Try changing the value stored in height to see the other results.

height <- 5

if (height == 5) { 
  
   print("We're the same height")
  
} else if (height > 7 & height < 10) { 
  
   print("You're tall!")
  
} else if (height == 5) {
  
   print("Wow, it's still 5!")
  
} else {
  
   print("Your height is different")
  
}
[1] "We're the same height"

A good practical example of if and else statements can be seen here PHE

4.2 Loops and Conditional Statements

We can combine a loop with if else statements.

  • We have a vector, with values from 1-7 and assign it to number_of_takeaways_ordered.

  • And a for loop with an if and else statement which shows that if you order more than 2 takeaways a week it will print "too many" and if you order less than two it prints "enough".

number_of_takeaways_ordered <- c(1, 2, 3, 4, 5, 6, 7)

for (number in number_of_takeaways_ordered) { 
  
  print(number)
  
  if (number > 3) { 
    
    print("too many")
    
  } else { 
    
  print("enough")

# We have a loop, and else if statements, which means we 
# need to careful about our closing brackets!
  }
}
[1] 1
[1] "enough"
[1] 2
[1] "enough"
[1] 3
[1] "enough"
[1] 4
[1] "too many"
[1] 5
[1] "too many"
[1] 6
[1] "too many"
[1] 7
[1] "too many"

4.2.1 Extension Exercise: FizzBuzz

  1. Can you write a loop with an if, else if and else statement, that loops through the numbers from 1 to 30.

    If the number is a multiple of both 3 and 5, print “FizzBuzz”.

    If the number is a multiple of 3, print the word “Fizz”.

    If the number is a multiple of 5, print the word “Buzz”.

    For all other values print the number.

Note: You can use the modulus operator for this - this is the %% in R, and shows us our remainder.

For example checking if something is divisible by 3 we can do:

# Example of Modulo
# Returns FALSE (10 / 3 has a remainder of 1 - so does not have a remainder of 0)

10 %% 3 == 0
[1] FALSE
# Second example of Modulo
# Returns TRUE (9 / 3 has a remainder of 0)

9 %% 3 == 0
[1] TRUE
for (each_number in vector) { 
  
    if (first_conditions) {
    
        output
    
  } else if (second_condition) {
    
             output
    
  } else if (third_condition) { 
    
             output
    
  } else {
    
            print(each_number)
    
  }
  
}
# Creating a loop with an with an `if`, `else if` and `else` statement

for (each_number in 1:30) { 
  
  if (each_number %% 3 == 0 & each_number %% 5 == 0) {
    
      print("FizzBuzz")
    
  } else if (each_number %% 3 == 0) {
    
      print("Fizz")
    
  } else if (each_number %% 5 == 0) { 
    
      print("Buzz")
    
  } else {
    
      print(each_number)
    
  }
  
}
[1] 1
[1] 2
[1] "Fizz"
[1] 4
[1] "Buzz"
[1] "Fizz"
[1] 7
[1] 8
[1] "Fizz"
[1] "Buzz"
[1] 11
[1] "Fizz"
[1] 13
[1] 14
[1] "FizzBuzz"
[1] 16
[1] 17
[1] "Fizz"
[1] 19
[1] "Buzz"
[1] "Fizz"
[1] 22
[1] 23
[1] "Fizz"
[1] "Buzz"
[1] 26
[1] "Fizz"
[1] 28
[1] 29
[1] "FizzBuzz"
# We have a loop, and else if statements, which means we 
# need to careful about our closing brackets!

4.3 dplyr if_else()

We have seen how we can use the if statement to create branching paths in our code.

A singular if statement is useful if we need to perform different code based on a single condition, but it’s not useful in cases where there are a lot of values that need comparison, such as in a tibble.

dplyr has a function called if_else()

The if_else() function is designed to be used with multiple values, like with a vector or a column in a tibble.

Lets look at the help information on the function to see what arguments we can specify.

I would strongly advise to look at the R help documentation when you come across a function you have not used before.

# Loading packages

library(dplyr)

# Looking at the help documentation

?dplyr::if_else
starting httpd help server ... done

Looking at help documentation for the function, we can see that it needs 3 main arguments.

  1. condition. A statement (e.g. comparison operator) that evaluates to TRUE or FALSE. Instead of passing it a single value, you need to give it a specific column in the data.

  2. true The value that the new column should take if the comparison operator is TRUE. This can be anything that we specify.

  3. false The value that the new column should take if the comparison operator is FALSE. Like the second argument, it can be anything you specify.

Lets look at an example.

We will use the Titanic dataset, lets see if there is any missing values in our dataset.

Here I am using the base R function anyNA, which will return TRUE if there are any missing values and FALSE if there are no missing values.

# Checks if there are missing values in our data

anyNA(titanic)
[1] TRUE

Now that we know they are missing values in the data, we can use the colSums() function or the summary() function to see how many missing values we have.

# Here I am using the colSums() function, which gives the sum of columns
# Inside the function, I am using the is.na() function which checks if 
# the data is a missing value

colSums(is.na(titanic))
           pclass          survived name_of_passenger  sex_of_passenger 
                0                 0                 0                 0 
 age_of_passenger             sibsp             parch            ticket 
              267                 0                 0                 0 
             fare             cabin          embarked 
                1              1014                 2 
# Summary of our data
# This is showing summary data for the age_of_passenger column
# Part of the summary data shows number of missing values.

summary(titanic$age_of_passenger)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.1667 21.0000 28.0000 29.7943 38.8750 80.0000     267 

We can see that the column age_of_passenger has 267 missing values.

We can impute (populate) our missing values using the dplyr if_else function.

Here we are going to use the mean value for age_of_passenger.

Please refer to the Editing and Imputation Course for details on how to best impute your missing data, this is for demonstration purposes only.

The mutate() function from the dplyr package is useful in creating a new variable. This is covered in detail in chapter 4 of the Introduction to R course.

We don’t necessarily want to change the original column so we instead create a new column which contains the age data, but with the missing values imputed.

Here my new column name is imputed_age_of_passenger.

The if_else() function takes that single comparison operator condition = is.na(age_of_passenger) and vectorise it.

# Dealing with missing values
# if the value is missing we can fill it
# if not we can use the original value

imputed_titanic <- titanic %>%
                      # New column name               # Condition to be met             
      dplyr::mutate(imputed_age_of_passenger = if_else(condition = is.na(age_of_passenger), 
                                                       
                                                      # Response if true
                                                       true = mean(age_of_passenger,na.rm =TRUE),
                                                       
                                                      # Response if false
                                                       false = age_of_passenger))
                                                         

# To display the resulting number of missing values
colSums(is.na(imputed_titanic))
                  pclass                 survived        name_of_passenger 
                       0                        0                        0 
        sex_of_passenger         age_of_passenger                    sibsp 
                       0                      267                        0 
                   parch                   ticket                     fare 
                       0                        0                        1 
                   cabin                 embarked imputed_age_of_passenger 
                    1014                        2                        0 
pclass survived name_of_passenger sex_of_passenger age_of_passenger imputed_age_of_passenger sibsp parch ticket fare cabin embarked
1 1 Allen, Miss. Elisabeth Walton female 29.0000 29.00000 0 0 24160 211.3375 B5 S
1 1 Allison, Master. Hudson Trevor male 0.9167 0.91670 1 2 113781 151.5500 C22 C26 S
1 0 Allison, Miss. Helen Loraine female 2.0000 2.00000 1 2 113781 151.5500 C22 C26 S
1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 30.00000 1 2 113781 151.5500 C22 C26 S
1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 25.00000 1 2 113781 151.5500 C22 C26 S
1 1 Anderson, Mr. Harry male 48.0000 48.00000 0 0 19952 26.5500 E12 S
1 1 Andrews, Miss. Kornelia Theodosia female 63.0000 63.00000 1 0 13502 77.9583 D7 S
1 0 Andrews, Mr. Thomas Jr male NA 29.79431 0 0 112050 0.0000 A36 S
1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female NA 29.79431 2 0 11769 51.4792 C101 S
1 0 Artagaveytia, Mr. Ramon male NA 29.79431 0 0 PC 17609 49.5042 NA C
1 0 Astor, Col. John Jacob male NA 29.79431 1 0 PC 17757 227.5250 C62 C64 C
1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0000 18.00000 1 0 PC 17757 227.5250 C62 C64 C
1 1 Aubart, Mme. Leontine Pauline female 24.0000 24.00000 0 0 PC 17477 69.3000 B35 C
1 1 Barber, Miss. Ellen "Nellie" female 26.0000 26.00000 0 0 19877 78.8500 NA S
1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0000 80.00000 0 0 27042 30.0000 A23 S
1 0 Baumann, Mr. John D male NA 29.79431 0 0 PC 17318 25.9250 NA S
1 0 Baxter, Mr. Quigg Edmond male 24.0000 24.00000 0 1 PC 17558 247.5208 B58 B60 C
1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0000 50.00000 0 1 PC 17558 247.5208 B58 B60 C
1 1 Bazzani, Miss. Albina female 32.0000 32.00000 0 0 11813 76.2917 D15 C
1 0 Beattie, Mr. Thomson male 36.0000 36.00000 0 0 13050 75.2417 C6 C
1 1 Beckwith, Mr. Richard Leonard male 37.0000 37.00000 1 1 11751 52.5542 D35 S
1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0000 47.00000 1 1 11751 52.5542 D35 S
1 1 Behr, Mr. Karl Howell male 26.0000 26.00000 0 0 111369 30.0000 C148 C
1 1 Bidois, Miss. Rosalie female 42.0000 42.00000 0 0 PC 17757 227.5250 NA C
1 1 Bird, Miss. Ellen female 29.0000 29.00000 0 0 PC 17483 221.7792 C97 S
1 0 Birnbaum, Mr. Jakob male 25.0000 25.00000 0 0 13905 26.0000 NA C
1 1 Bishop, Mr. Dickinson H male 25.0000 25.00000 1 0 11967 91.0792 B49 C
1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.0000 19.00000 1 0 11967 91.0792 B49 C
1 1 Bissette, Miss. Amelia female 35.0000 35.00000 0 0 PC 17760 135.6333 C99 S
1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.0000 28.00000 0 0 110564 26.5500 C52 S
1 0 Blackwell, Mr. Stephen Weart male 45.0000 45.00000 0 0 113784 35.5000 T S
1 1 Blank, Mr. Henry male 40.0000 40.00000 0 0 112277 31.0000 A31 C
1 1 Bonnell, Miss. Caroline female 30.0000 30.00000 0 0 36928 164.8667 C7 S
1 1 Bonnell, Miss. Elizabeth female 58.0000 58.00000 0 0 113783 26.5500 C103 S
1 0 Borebank, Mr. John James male 42.0000 42.00000 0 0 110489 26.5500 D22 S
1 1 Bowen, Miss. Grace Scott female 45.0000 45.00000 0 0 PC 17608 262.3750 NA C
1 1 Bowerman, Miss. Elsie Edith female 22.0000 22.00000 0 1 113505 55.0000 E33 S
1 1 Bradley, Mr. George ("George Arthur Brayton") male NA 29.79431 0 0 111427 26.5500 NA S
1 0 Brady, Mr. John Bertram male 41.0000 41.00000 0 0 113054 30.5000 A21 S
1 0 Brandeis, Mr. Emil male 48.0000 48.00000 0 0 PC 17591 50.4958 B10 C
1 0 Brewe, Dr. Arthur Jackson male NA 29.79431 0 0 112379 39.6000 NA C
1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44.0000 44.00000 0 0 PC 17610 27.7208 B4 C
1 1 Brown, Mrs. John Murray (Caroline Lane Lamson) female 59.0000 59.00000 2 0 11769 51.4792 C101 S
1 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) female 60.0000 60.00000 0 0 11813 76.2917 D15 C
1 1 Burns, Miss. Elizabeth Margaret female 41.0000 41.00000 0 0 16966 134.5000 E40 C
1 0 Butt, Major. Archibald Willingham male 45.0000 45.00000 0 0 113050 26.5500 B38 S
1 0 Cairns, Mr. Alexander male NA 29.79431 0 0 113798 31.0000 NA S
1 1 Calderhead, Mr. Edward Pennington male 42.0000 42.00000 0 0 PC 17476 26.2875 E24 S
1 1 Candee, Mrs. Edward (Helen Churchill Hungerford) female 53.0000 53.00000 0 0 PC 17606 27.4458 NA C
1 1 Cardeza, Mr. Thomas Drake Martinez male 36.0000 36.00000 0 1 PC 17755 512.3292 B51 B53 B55 C
1 1 Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake) female 58.0000 58.00000 0 1 PC 17755 512.3292 B51 B53 B55 C
1 0 Carlsson, Mr. Frans Olof male 33.0000 33.00000 0 0 695 5.0000 B51 B53 B55 S
1 0 Carrau, Mr. Francisco M male 28.0000 28.00000 0 0 113059 47.1000 NA S
1 0 Carrau, Mr. Jose Pedro male 17.0000 17.00000 0 0 113059 47.1000 NA S
1 1 Carter, Master. William Thornton II male 11.0000 11.00000 1 2 113760 120.0000 B96 B98 S
1 1 Carter, Miss. Lucile Polk female 14.0000 14.00000 1 2 113760 120.0000 B96 B98 S
1 1 Carter, Mr. William Ernest male 36.0000 36.00000 1 2 113760 120.0000 B96 B98 S
1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36.0000 36.00000 1 2 113760 120.0000 B96 B98 S
1 0 Case, Mr. Howard Brown male 49.0000 49.00000 0 0 19924 26.0000 NA S
1 1 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) female NA 29.79431 0 0 17770 27.7208 NA C
1 0 Cavendish, Mr. Tyrell William male 36.0000 36.00000 1 0 19877 78.8500 C46 S
1 1 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) female 76.0000 76.00000 1 0 19877 78.8500 C46 S
1 0 Chaffee, Mr. Herbert Fuller male 46.0000 46.00000 1 0 W.E.P. 5734 61.1750 E31 S
1 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood) female 47.0000 47.00000 1 0 W.E.P. 5734 61.1750 E31 S
1 1 Chambers, Mr. Norman Campbell male 27.0000 27.00000 1 0 113806 53.1000 E8 S
1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.0000 33.00000 1 0 113806 53.1000 E8 S
1 1 Chaudanson, Miss. Victorine female 36.0000 36.00000 0 0 PC 17608 262.3750 B61 C
1 1 Cherry, Miss. Gladys female 30.0000 30.00000 0 0 110152 86.5000 B77 S
1 1 Chevre, Mr. Paul Romaine male 45.0000 45.00000 0 0 PC 17594 29.7000 A9 C
1 1 Chibnall, Mrs. (Edith Martha Bowerman) female NA 29.79431 0 1 113505 55.0000 E33 S
1 0 Chisholm, Mr. Roderick Robert Crispin male NA 29.79431 0 0 112051 0.0000 NA S
1 0 Clark, Mr. Walter Miller male 27.0000 27.00000 1 0 13508 136.7792 C89 C
1 1 Clark, Mrs. Walter Miller (Virginia McDowell) female 26.0000 26.00000 1 0 13508 136.7792 C89 C
1 1 Cleaver, Miss. Alice female 22.0000 22.00000 0 0 113781 151.5500 NA S
1 0 Clifford, Mr. George Quincy male NA 29.79431 0 0 110465 52.0000 A14 S
1 0 Colley, Mr. Edward Pomeroy male 47.0000 47.00000 0 0 5727 25.5875 E58 S
1 1 Compton, Miss. Sara Rebecca female 39.0000 39.00000 1 1 PC 17756 83.1583 E49 C
1 0 Compton, Mr. Alexander Taylor Jr male 37.0000 37.00000 1 1 PC 17756 83.1583 E52 C
1 1 Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) female 64.0000 64.00000 0 2 PC 17756 83.1583 E45 C
1 1 Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) female 55.0000 55.00000 2 0 11770 25.7000 C101 S
1 0 Crafton, Mr. John Bertram male NA 29.79431 0 0 113791 26.5500 NA S
1 0 Crosby, Capt. Edward Gifford male 70.0000 70.00000 1 1 WE/P 5735 71.0000 B22 S
1 1 Crosby, Miss. Harriet R female 36.0000 36.00000 0 2 WE/P 5735 71.0000 B22 S
1 1 Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) female 64.0000 64.00000 1 1 112901 26.5500 B26 S
1 0 Cumings, Mr. John Bradley male 39.0000 39.00000 1 0 PC 17599 71.2833 C85 C
1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0000 38.00000 1 0 PC 17599 71.2833 C85 C
1 1 Daly, Mr. Peter Denis male 51.0000 51.00000 0 0 113055 26.5500 E17 S
1 1 Daniel, Mr. Robert Williams male 27.0000 27.00000 0 0 113804 30.5000 NA S
1 1 Daniels, Miss. Sarah female 33.0000 33.00000 0 0 113781 151.5500 NA S
1 0 Davidson, Mr. Thornton male 31.0000 31.00000 1 0 F.C. 12750 52.0000 B71 S
1 1 Davidson, Mrs. Thornton (Orian Hays) female 27.0000 27.00000 1 2 F.C. 12750 52.0000 B71 S
1 1 Dick, Mr. Albert Adrian male 31.0000 31.00000 1 0 17474 57.0000 B20 S
1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.0000 17.00000 1 0 17474 57.0000 B20 S
1 1 Dodge, Dr. Washington male 53.0000 53.00000 1 1 33638 81.8583 A34 S
1 1 Dodge, Master. Washington male 4.0000 4.00000 0 2 33638 81.8583 A34 S
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3 0 Risien, Mr. Samuel Beard male NA 29.79431 0 0 364498 14.5000 NA S
3 0 Risien, Mrs. Samuel (Emma) female NA 29.79431 0 0 364498 14.5000 NA S
3 0 Robins, Mr. Alexander A male 50.0000 50.00000 1 0 A/5. 3337 14.5000 NA S
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3 1 Roth, Miss. Sarah A female NA 29.79431 0 0 342712 8.0500 NA S
3 0 Rouse, Mr. Richard Henry male 50.0000 50.00000 0 0 A/5 3594 8.0500 NA S
3 0 Rush, Mr. Alfred George John male 16.0000 16.00000 0 0 A/4. 20589 8.0500 NA S
3 1 Ryan, Mr. Edward male NA 29.79431 0 0 383162 7.7500 NA Q
3 0 Ryan, Mr. Patrick male NA 29.79431 0 0 371110 24.1500 NA Q
3 0 Saad, Mr. Amin male NA 29.79431 0 0 2671 7.2292 NA C
3 0 Saad, Mr. Khalil male 25.0000 25.00000 0 0 2672 7.2250 NA C
3 0 Saade, Mr. Jean Nassr male NA 29.79431 0 0 2676 7.2250 NA C
3 0 Sadlier, Mr. Matthew male NA 29.79431 0 0 367655 7.7292 NA Q
3 0 Sadowitz, Mr. Harry male NA 29.79431 0 0 LP 1588 7.5750 NA S
3 0 Saether, Mr. Simon Sivertsen male 38.5000 38.50000 0 0 SOTON/O.Q. 3101262 7.2500 NA S
3 0 Sage, Master. Thomas Henry male NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Master. William Henry male 14.5000 14.50000 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Miss. Ada female NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Miss. Constance Gladys female NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Miss. Dorothy Edith "Dolly" female NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Miss. Stella Anna female NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Mr. Douglas Bullen male NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Mr. Frederick male NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Mr. George John Jr male NA 29.79431 8 2 CA. 2343 69.5500 NA S
3 0 Sage, Mr. John George male NA 29.79431 1 9 CA. 2343 69.5500 NA S
3 0 Sage, Mrs. John (Annie Bullen) female NA 29.79431 1 9 CA. 2343 69.5500 NA S
3 0 Salander, Mr. Karl Johan male 24.0000 24.00000 0 0 7266 9.3250 NA S
3 1 Salkjelsvik, Miss. Anna Kristine female 21.0000 21.00000 0 0 343120 7.6500 NA S
3 0 Salonen, Mr. Johan Werner male 39.0000 39.00000 0 0 3101296 7.9250 NA S
3 0 Samaan, Mr. Elias male NA 29.79431 2 0 2662 21.6792 NA C
3 0 Samaan, Mr. Hanna male NA 29.79431 2 0 2662 21.6792 NA C
3 0 Samaan, Mr. Youssef male NA 29.79431 2 0 2662 21.6792 NA C
3 1 Sandstrom, Miss. Beatrice Irene female 1.0000 1.00000 1 1 PP 9549 16.7000 G6 S
3 1 Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson) female 24.0000 24.00000 0 2 PP 9549 16.7000 G6 S
3 1 Sandstrom, Miss. Marguerite Rut female 4.0000 4.00000 1 1 PP 9549 16.7000 G6 S
3 1 Sap, Mr. Julius male 25.0000 25.00000 0 0 345768 9.5000 NA S
3 0 Saundercock, Mr. William Henry male 20.0000 20.00000 0 0 A/5. 2151 8.0500 NA S
3 0 Sawyer, Mr. Frederick Charles male 24.5000 24.50000 0 0 342826 8.0500 NA S
3 0 Scanlan, Mr. James male NA 29.79431 0 0 36209 7.7250 NA Q
3 0 Sdycoff, Mr. Todor male NA 29.79431 0 0 349222 7.8958 NA S
3 0 Shaughnessy, Mr. Patrick male NA 29.79431 0 0 370374 7.7500 NA Q
3 1 Sheerlinck, Mr. Jan Baptist male 29.0000 29.00000 0 0 345779 9.5000 NA S
3 0 Shellard, Mr. Frederick William male NA 29.79431 0 0 C.A. 6212 15.1000 NA S
3 1 Shine, Miss. Ellen Natalia female NA 29.79431 0 0 330968 7.7792 NA Q
3 0 Shorney, Mr. Charles Joseph male NA 29.79431 0 0 374910 8.0500 NA S
3 0 Simmons, Mr. John male NA 29.79431 0 0 SOTON/OQ 392082 8.0500 NA S
3 0 Sirayanian, Mr. Orsen male 22.0000 22.00000 0 0 2669 7.2292 NA C
3 0 Sirota, Mr. Maurice male NA 29.79431 0 0 392092 8.0500 NA S
3 0 Sivic, Mr. Husein male 40.0000 40.00000 0 0 349251 7.8958 NA S
3 0 Sivola, Mr. Antti Wilhelm male 21.0000 21.00000 0 0 STON/O 2. 3101280 7.9250 NA S
3 1 Sjoblom, Miss. Anna Sofia female 18.0000 18.00000 0 0 3101265 7.4958 NA S
3 0 Skoog, Master. Harald male 4.0000 4.00000 3 2 347088 27.9000 NA S
3 0 Skoog, Master. Karl Thorsten male 10.0000 10.00000 3 2 347088 27.9000 NA S
3 0 Skoog, Miss. Mabel female 9.0000 9.00000 3 2 347088 27.9000 NA S
3 0 Skoog, Miss. Margit Elizabeth female 2.0000 2.00000 3 2 347088 27.9000 NA S
3 0 Skoog, Mr. Wilhelm male 40.0000 40.00000 1 4 347088 27.9000 NA S
3 0 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45.0000 45.00000 1 4 347088 27.9000 NA S
3 0 Slabenoff, Mr. Petco male NA 29.79431 0 0 349214 7.8958 NA S
3 0 Slocovski, Mr. Selman Francis male NA 29.79431 0 0 SOTON/OQ 392086 8.0500 NA S
3 0 Smiljanic, Mr. Mile male NA 29.79431 0 0 315037 8.6625 NA S
3 0 Smith, Mr. Thomas male NA 29.79431 0 0 384461 7.7500 NA Q
3 1 Smyth, Miss. Julia female NA 29.79431 0 0 335432 7.7333 NA Q
3 0 Soholt, Mr. Peter Andreas Lauritz Andersen male 19.0000 19.00000 0 0 348124 7.6500 F G73 S
3 0 Somerton, Mr. Francis William male 30.0000 30.00000 0 0 A.5. 18509 8.0500 NA S
3 0 Spector, Mr. Woolf male NA 29.79431 0 0 A.5. 3236 8.0500 NA S
3 0 Spinner, Mr. Henry John male 32.0000 32.00000 0 0 STON/OQ. 369943 8.0500 NA S
3 0 Staneff, Mr. Ivan male NA 29.79431 0 0 349208 7.8958 NA S
3 0 Stankovic, Mr. Ivan male 33.0000 33.00000 0 0 349239 8.6625 NA C
3 1 Stanley, Miss. Amy Zillah Elsie female 23.0000 23.00000 0 0 CA. 2314 7.5500 NA S
3 0 Stanley, Mr. Edward Roland male 21.0000 21.00000 0 0 A/4 45380 8.0500 NA S
3 0 Storey, Mr. Thomas male 60.5000 60.50000 0 0 3701 NA NA S
3 0 Stoytcheff, Mr. Ilia male 19.0000 19.00000 0 0 349205 7.8958 NA S
3 0 Strandberg, Miss. Ida Sofia female 22.0000 22.00000 0 0 7553 9.8375 NA S
3 1 Stranden, Mr. Juho male 31.0000 31.00000 0 0 STON/O 2. 3101288 7.9250 NA S
3 0 Strilic, Mr. Ivan male 27.0000 27.00000 0 0 315083 8.6625 NA S
3 0 Strom, Miss. Telma Matilda female 2.0000 2.00000 0 1 347054 10.4625 G6 S
3 0 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29.0000 29.00000 1 1 347054 10.4625 G6 S
3 1 Sunderland, Mr. Victor Francis male 16.0000 16.00000 0 0 SOTON/OQ 392089 8.0500 NA S
3 1 Sundman, Mr. Johan Julian male 44.0000 44.00000 0 0 STON/O 2. 3101269 7.9250 NA S
3 0 Sutehall, Mr. Henry Jr male 25.0000 25.00000 0 0 SOTON/OQ 392076 7.0500 NA S
3 0 Svensson, Mr. Johan male 74.0000 74.00000 0 0 347060 7.7750 NA S
3 1 Svensson, Mr. Johan Cervin male 14.0000 14.00000 0 0 7538 9.2250 NA S
3 0 Svensson, Mr. Olof male 24.0000 24.00000 0 0 350035 7.7958 NA S
3 1 Tenglin, Mr. Gunnar Isidor male 25.0000 25.00000 0 0 350033 7.7958 NA S
3 0 Theobald, Mr. Thomas Leonard male 34.0000 34.00000 0 0 363294 8.0500 NA S
3 1 Thomas, Master. Assad Alexander male 0.4167 0.41670 0 1 2625 8.5167 NA C
3 0 Thomas, Mr. Charles P male NA 29.79431 1 0 2621 6.4375 NA C
3 0 Thomas, Mr. John male NA 29.79431 0 0 2681 6.4375 NA C
3 0 Thomas, Mr. Tannous male NA 29.79431 0 0 2684 7.2250 NA C
3 1 Thomas, Mrs. Alexander (Thamine "Thelma") female 16.0000 16.00000 1 1 2625 8.5167 NA C
3 0 Thomson, Mr. Alexander Morrison male NA 29.79431 0 0 32302 8.0500 NA S
3 0 Thorneycroft, Mr. Percival male NA 29.79431 1 0 376564 16.1000 NA S
3 1 Thorneycroft, Mrs. Percival (Florence Kate White) female NA 29.79431 1 0 376564 16.1000 NA S
3 0 Tikkanen, Mr. Juho male 32.0000 32.00000 0 0 STON/O 2. 3101293 7.9250 NA S
3 0 Tobin, Mr. Roger male NA 29.79431 0 0 383121 7.7500 F38 Q
3 0 Todoroff, Mr. Lalio male NA 29.79431 0 0 349216 7.8958 NA S
3 0 Tomlin, Mr. Ernest Portage male 30.5000 30.50000 0 0 364499 8.0500 NA S
3 0 Torber, Mr. Ernst William male 44.0000 44.00000 0 0 364511 8.0500 NA S
3 0 Torfa, Mr. Assad male NA 29.79431 0 0 2673 7.2292 NA C
3 1 Tornquist, Mr. William Henry male 25.0000 25.00000 0 0 LINE 0.0000 NA S
3 0 Toufik, Mr. Nakli male NA 29.79431 0 0 2641 7.2292 NA C
3 1 Touma, Master. Georges Youssef male 7.0000 7.00000 1 1 2650 15.2458 NA C
3 1 Touma, Miss. Maria Youssef female 9.0000 9.00000 1 1 2650 15.2458 NA C
3 1 Touma, Mrs. Darwis (Hanne Youssef Razi) female 29.0000 29.00000 0 2 2650 15.2458 NA C
3 0 Turcin, Mr. Stjepan male 36.0000 36.00000 0 0 349247 7.8958 NA S
3 1 Turja, Miss. Anna Sofia female 18.0000 18.00000 0 0 4138 9.8417 NA S
3 1 Turkula, Mrs. (Hedwig) female 63.0000 63.00000 0 0 4134 9.5875 NA S
3 0 van Billiard, Master. James William male NA 29.79431 1 1 A/5. 851 14.5000 NA S
3 0 van Billiard, Master. Walter John male 11.5000 11.50000 1 1 A/5. 851 14.5000 NA S
3 0 van Billiard, Mr. Austin Blyler male 40.5000 40.50000 0 2 A/5. 851 14.5000 NA S
3 0 Van Impe, Miss. Catharina female 10.0000 10.00000 0 2 345773 24.1500 NA S
3 0 Van Impe, Mr. Jean Baptiste male 36.0000 36.00000 1 1 345773 24.1500 NA S
3 0 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30.0000 30.00000 1 1 345773 24.1500 NA S
3 0 van Melkebeke, Mr. Philemon male NA 29.79431 0 0 345777 9.5000 NA S
3 0 Vande Velde, Mr. Johannes Joseph male 33.0000 33.00000 0 0 345780 9.5000 NA S
3 0 Vande Walle, Mr. Nestor Cyriel male 28.0000 28.00000 0 0 345770 9.5000 NA S
3 0 Vanden Steen, Mr. Leo Peter male 28.0000 28.00000 0 0 345783 9.5000 NA S
3 0 Vander Cruyssen, Mr. Victor male 47.0000 47.00000 0 0 345765 9.0000 NA S
3 0 Vander Planke, Miss. Augusta Maria female 18.0000 18.00000 2 0 345764 18.0000 NA S
3 0 Vander Planke, Mr. Julius male 31.0000 31.00000 3 0 345763 18.0000 NA S
3 0 Vander Planke, Mr. Leo Edmondus male 16.0000 16.00000 2 0 345764 18.0000 NA S
3 0 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.0000 31.00000 1 0 345763 18.0000 NA S
3 1 Vartanian, Mr. David male 22.0000 22.00000 0 0 2658 7.2250 NA C
3 0 Vendel, Mr. Olof Edvin male 20.0000 20.00000 0 0 350416 7.8542 NA S
3 0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0000 14.00000 0 0 350406 7.8542 NA S
3 0 Vovk, Mr. Janko male 22.0000 22.00000 0 0 349252 7.8958 NA S
3 0 Waelens, Mr. Achille male 22.0000 22.00000 0 0 345767 9.0000 NA S
3 0 Ware, Mr. Frederick male NA 29.79431 0 0 359309 8.0500 NA S
3 0 Warren, Mr. Charles William male NA 29.79431 0 0 C.A. 49867 7.5500 NA S
3 0 Webber, Mr. James male NA 29.79431 0 0 SOTON/OQ 3101316 8.0500 NA S
3 0 Wenzel, Mr. Linhart male 32.5000 32.50000 0 0 345775 9.5000 NA S
3 1 Whabee, Mrs. George Joseph (Shawneene Abi-Saab) female 38.0000 38.00000 0 0 2688 7.2292 NA C
3 0 Widegren, Mr. Carl/Charles Peter male 51.0000 51.00000 0 0 347064 7.7500 NA S
3 0 Wiklund, Mr. Jakob Alfred male 18.0000 18.00000 1 0 3101267 6.4958 NA S
3 0 Wiklund, Mr. Karl Johan male 21.0000 21.00000 1 0 3101266 6.4958 NA S
3 1 Wilkes, Mrs. James (Ellen Needs) female 47.0000 47.00000 1 0 363272 7.0000 NA S
3 0 Willer, Mr. Aaron ("Abi Weller") male NA 29.79431 0 0 3410 8.7125 NA S
3 0 Willey, Mr. Edward male NA 29.79431 0 0 S.O./P.P. 751 7.5500 NA S
3 0 Williams, Mr. Howard Hugh "Harry" male NA 29.79431 0 0 A/5 2466 8.0500 NA S
3 0 Williams, Mr. Leslie male 28.5000 28.50000 0 0 54636 16.1000 NA S
3 0 Windelov, Mr. Einar male 21.0000 21.00000 0 0 SOTON/OQ 3101317 7.2500 NA S
3 0 Wirz, Mr. Albert male 27.0000 27.00000 0 0 315154 8.6625 NA S
3 0 Wiseman, Mr. Phillippe male NA 29.79431 0 0 A/4. 34244 7.2500 NA S
3 0 Wittevrongel, Mr. Camille male 36.0000 36.00000 0 0 345771 9.5000 NA S
3 0 Yasbeck, Mr. Antoni male 27.0000 27.00000 1 0 2659 14.4542 NA C
3 1 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0000 15.00000 1 0 2659 14.4542 NA C
3 0 Youseff, Mr. Gerious male 45.5000 45.50000 0 0 2628 7.2250 NA C
3 0 Yousif, Mr. Wazli male NA 29.79431 0 0 2647 7.2250 NA C
3 0 Yousseff, Mr. Gerious male NA 29.79431 0 0 2627 14.4583 NA C
3 0 Zabour, Miss. Hileni female 14.5000 14.50000 1 0 2665 14.4542 NA C
3 0 Zabour, Miss. Thamine female NA 29.79431 1 0 2665 14.4542 NA C
3 0 Zakarian, Mr. Mapriededer male 26.5000 26.50000 0 0 2656 7.2250 NA C
3 0 Zakarian, Mr. Ortin male 27.0000 27.00000 0 0 2670 7.2250 NA C
3 0 Zimmerman, Mr. Leo male 29.0000 29.00000 0 0 315082 7.8750 NA S

4.4 dplyr case_when()

So far we have looked at else if to add more options to an if statement.

We have also used the the if_else() function, but it works best with simpler conditions that evaluate to TRUE or FALSE.

We can put multiple if_else() commands inside each other, but this makes code hard to read and is not recommended.

If we are dealing with multiple complex conditions dplyr also has a function called case_when()

You can think of the case_when() function as a generalisation of the if_else() function, which incorporates more branches.

This command takes multiple conditions and tests them in order.

case_when() takes a conditional command in the same format as the first command in if_else(), however only the action for if the condition is true is given, separated with a tilde ~.

Lets look at an example.

Consider you wanted to add in a new column in the titanic dataset of the Countries in which the passengers embarked.

So those who embarked in

  • “S” (Southampton) it should say England

  • “C” (Cherbourg) should say France

  • “Q” (Queenstown) should say Ireland

# Here I am using case_when within the mutate function as I want to create a new column within my dataset
# My new column name is country_embarked
# To use case_when(), you specify the condition first
# Then the ~ tilde for the action when the condition is met
# We can also specify what we want to occur if none of the conditions match - the TRUE value

case_when_example <- titanic %>% 
  dplyr::mutate(
    country_embarked = dplyr::case_when( 
                     # condition        # action
                      embarked == "S"  ~ "England",
                      embarked == "C" ~ "France",
                      embarked == "Q" ~ "Ireland",
                     # This is what is going to happen if none of the set
                     # conditions are met
                      TRUE ~ "Unknown") 
  )
embarked country_embarked pclass survived name_of_passenger sex_of_passenger age_of_passenger sibsp parch ticket fare cabin
S England 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5
S England 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26
S England 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26
S England 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26
S England 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26
S England 1 1 Anderson, Mr. Harry male 48.0000 0 0 19952 26.5500 E12
S England 1 1 Andrews, Miss. Kornelia Theodosia female 63.0000 1 0 13502 77.9583 D7
S England 1 0 Andrews, Mr. Thomas Jr male NA 0 0 112050 0.0000 A36
S England 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female NA 2 0 11769 51.4792 C101
C France 1 0 Artagaveytia, Mr. Ramon male NA 0 0 PC 17609 49.5042 NA
C France 1 0 Astor, Col. John Jacob male NA 1 0 PC 17757 227.5250 C62 C64
C France 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0000 1 0 PC 17757 227.5250 C62 C64
C France 1 1 Aubart, Mme. Leontine Pauline female 24.0000 0 0 PC 17477 69.3000 B35
S England 1 1 Barber, Miss. Ellen "Nellie" female 26.0000 0 0 19877 78.8500 NA
S England 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0000 0 0 27042 30.0000 A23
S England 1 0 Baumann, Mr. John D male NA 0 0 PC 17318 25.9250 NA
C France 1 0 Baxter, Mr. Quigg Edmond male 24.0000 0 1 PC 17558 247.5208 B58 B60
C France 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0000 0 1 PC 17558 247.5208 B58 B60
C France 1 1 Bazzani, Miss. Albina female 32.0000 0 0 11813 76.2917 D15
C France 1 0 Beattie, Mr. Thomson male 36.0000 0 0 13050 75.2417 C6
S England 1 1 Beckwith, Mr. Richard Leonard male 37.0000 1 1 11751 52.5542 D35
S England 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0000 1 1 11751 52.5542 D35
C France 1 1 Behr, Mr. Karl Howell male 26.0000 0 0 111369 30.0000 C148
C France 1 1 Bidois, Miss. Rosalie female 42.0000 0 0 PC 17757 227.5250 NA
S England 1 1 Bird, Miss. Ellen female 29.0000 0 0 PC 17483 221.7792 C97
C France 1 0 Birnbaum, Mr. Jakob male 25.0000 0 0 13905 26.0000 NA
C France 1 1 Bishop, Mr. Dickinson H male 25.0000 1 0 11967 91.0792 B49
C France 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.0000 1 0 11967 91.0792 B49
S England 1 1 Bissette, Miss. Amelia female 35.0000 0 0 PC 17760 135.6333 C99
S England 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.0000 0 0 110564 26.5500 C52
S England 1 0 Blackwell, Mr. Stephen Weart male 45.0000 0 0 113784 35.5000 T
C France 1 1 Blank, Mr. Henry male 40.0000 0 0 112277 31.0000 A31
S England 1 1 Bonnell, Miss. Caroline female 30.0000 0 0 36928 164.8667 C7
S England 1 1 Bonnell, Miss. Elizabeth female 58.0000 0 0 113783 26.5500 C103
S England 1 0 Borebank, Mr. John James male 42.0000 0 0 110489 26.5500 D22
C France 1 1 Bowen, Miss. Grace Scott female 45.0000 0 0 PC 17608 262.3750 NA
S England 1 1 Bowerman, Miss. Elsie Edith female 22.0000 0 1 113505 55.0000 E33
S England 1 1 Bradley, Mr. George ("George Arthur Brayton") male NA 0 0 111427 26.5500 NA
S England 1 0 Brady, Mr. John Bertram male 41.0000 0 0 113054 30.5000 A21
C France 1 0 Brandeis, Mr. Emil male 48.0000 0 0 PC 17591 50.4958 B10
C France 1 0 Brewe, Dr. Arthur Jackson male NA 0 0 112379 39.6000 NA
C France 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44.0000 0 0 PC 17610 27.7208 B4
S England 1 1 Brown, Mrs. John Murray (Caroline Lane Lamson) female 59.0000 2 0 11769 51.4792 C101
C France 1 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) female 60.0000 0 0 11813 76.2917 D15
C France 1 1 Burns, Miss. Elizabeth Margaret female 41.0000 0 0 16966 134.5000 E40
S England 1 0 Butt, Major. Archibald Willingham male 45.0000 0 0 113050 26.5500 B38
S England 1 0 Cairns, Mr. Alexander male NA 0 0 113798 31.0000 NA
S England 1 1 Calderhead, Mr. Edward Pennington male 42.0000 0 0 PC 17476 26.2875 E24
C France 1 1 Candee, Mrs. Edward (Helen Churchill Hungerford) female 53.0000 0 0 PC 17606 27.4458 NA
C France 1 1 Cardeza, Mr. Thomas Drake Martinez male 36.0000 0 1 PC 17755 512.3292 B51 B53 B55
C France 1 1 Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake) female 58.0000 0 1 PC 17755 512.3292 B51 B53 B55
S England 1 0 Carlsson, Mr. Frans Olof male 33.0000 0 0 695 5.0000 B51 B53 B55
S England 1 0 Carrau, Mr. Francisco M male 28.0000 0 0 113059 47.1000 NA
S England 1 0 Carrau, Mr. Jose Pedro male 17.0000 0 0 113059 47.1000 NA
S England 1 1 Carter, Master. William Thornton II male 11.0000 1 2 113760 120.0000 B96 B98
S England 1 1 Carter, Miss. Lucile Polk female 14.0000 1 2 113760 120.0000 B96 B98
S England 1 1 Carter, Mr. William Ernest male 36.0000 1 2 113760 120.0000 B96 B98
S England 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36.0000 1 2 113760 120.0000 B96 B98
S England 1 0 Case, Mr. Howard Brown male 49.0000 0 0 19924 26.0000 NA
C France 1 1 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) female NA 0 0 17770 27.7208 NA
S England 1 0 Cavendish, Mr. Tyrell William male 36.0000 1 0 19877 78.8500 C46
S England 1 1 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) female 76.0000 1 0 19877 78.8500 C46
S England 1 0 Chaffee, Mr. Herbert Fuller male 46.0000 1 0 W.E.P. 5734 61.1750 E31
S England 1 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood) female 47.0000 1 0 W.E.P. 5734 61.1750 E31
S England 1 1 Chambers, Mr. Norman Campbell male 27.0000 1 0 113806 53.1000 E8
S England 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.0000 1 0 113806 53.1000 E8
C France 1 1 Chaudanson, Miss. Victorine female 36.0000 0 0 PC 17608 262.3750 B61
S England 1 1 Cherry, Miss. Gladys female 30.0000 0 0 110152 86.5000 B77
C France 1 1 Chevre, Mr. Paul Romaine male 45.0000 0 0 PC 17594 29.7000 A9
S England 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female NA 0 1 113505 55.0000 E33
S England 1 0 Chisholm, Mr. Roderick Robert Crispin male NA 0 0 112051 0.0000 NA
C France 1 0 Clark, Mr. Walter Miller male 27.0000 1 0 13508 136.7792 C89
C France 1 1 Clark, Mrs. Walter Miller (Virginia McDowell) female 26.0000 1 0 13508 136.7792 C89
S England 1 1 Cleaver, Miss. Alice female 22.0000 0 0 113781 151.5500 NA
S England 1 0 Clifford, Mr. George Quincy male NA 0 0 110465 52.0000 A14
S England 1 0 Colley, Mr. Edward Pomeroy male 47.0000 0 0 5727 25.5875 E58
C France 1 1 Compton, Miss. Sara Rebecca female 39.0000 1 1 PC 17756 83.1583 E49
C France 1 0 Compton, Mr. Alexander Taylor Jr male 37.0000 1 1 PC 17756 83.1583 E52
C France 1 1 Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) female 64.0000 0 2 PC 17756 83.1583 E45
S England 1 1 Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) female 55.0000 2 0 11770 25.7000 C101
S England 1 0 Crafton, Mr. John Bertram male NA 0 0 113791 26.5500 NA
S England 1 0 Crosby, Capt. Edward Gifford male 70.0000 1 1 WE/P 5735 71.0000 B22
S England 1 1 Crosby, Miss. Harriet R female 36.0000 0 2 WE/P 5735 71.0000 B22
S England 1 1 Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) female 64.0000 1 1 112901 26.5500 B26
C France 1 0 Cumings, Mr. John Bradley male 39.0000 1 0 PC 17599 71.2833 C85
C France 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0000 1 0 PC 17599 71.2833 C85
S England 1 1 Daly, Mr. Peter Denis male 51.0000 0 0 113055 26.5500 E17
S England 1 1 Daniel, Mr. Robert Williams male 27.0000 0 0 113804 30.5000 NA
S England 1 1 Daniels, Miss. Sarah female 33.0000 0 0 113781 151.5500 NA
S England 1 0 Davidson, Mr. Thornton male 31.0000 1 0 F.C. 12750 52.0000 B71
S England 1 1 Davidson, Mrs. Thornton (Orian Hays) female 27.0000 1 2 F.C. 12750 52.0000 B71
S England 1 1 Dick, Mr. Albert Adrian male 31.0000 1 0 17474 57.0000 B20
S England 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.0000 1 0 17474 57.0000 B20
S England 1 1 Dodge, Dr. Washington male 53.0000 1 1 33638 81.8583 A34
S England 1 1 Dodge, Master. Washington male 4.0000 0 2 33638 81.8583 A34
S England 1 1 Dodge, Mrs. Washington (Ruth Vidaver) female 54.0000 1 1 33638 81.8583 A34
C France 1 0 Douglas, Mr. Walter Donald male 50.0000 1 0 PC 17761 106.4250 C86
C France 1 1 Douglas, Mrs. Frederick Charles (Mary Helene Baxter) female 27.0000 1 1 PC 17558 247.5208 B58 B60
C France 1 1 Douglas, Mrs. Walter Donald (Mahala Dutton) female 48.0000 1 0 PC 17761 106.4250 C86
C France 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") female 48.0000 1 0 11755 39.6000 A16
C France 1 1 Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") male 49.0000 1 0 PC 17485 56.9292 A20
C France 1 0 Dulles, Mr. William Crothers male 39.0000 0 0 PC 17580 29.7000 A18
C France 1 1 Earnshaw, Mrs. Boulton (Olive Potter) female 23.0000 0 1 11767 83.1583 C54
C France 1 1 Endres, Miss. Caroline Louise female 38.0000 0 0 PC 17757 227.5250 C45
C France 1 1 Eustis, Miss. Elizabeth Mussey female 54.0000 1 0 36947 78.2667 D20
C France 1 0 Evans, Miss. Edith Corse female 36.0000 0 0 PC 17531 31.6792 A29
S England 1 0 Farthing, Mr. John male NA 0 0 PC 17483 221.7792 C95
S England 1 1 Flegenheim, Mrs. Alfred (Antoinette) female NA 0 0 PC 17598 31.6833 NA
C France 1 1 Fleming, Miss. Margaret female NA 0 0 17421 110.8833 NA
S England 1 1 Flynn, Mr. John Irwin ("Irving") male 36.0000 0 0 PC 17474 26.3875 E25
C France 1 0 Foreman, Mr. Benjamin Laventall male 30.0000 0 0 113051 27.7500 C111
S England 1 1 Fortune, Miss. Alice Elizabeth female 24.0000 3 2 19950 263.0000 C23 C25 C27
S England 1 1 Fortune, Miss. Ethel Flora female 28.0000 3 2 19950 263.0000 C23 C25 C27
S England 1 1 Fortune, Miss. Mabel Helen female 23.0000 3 2 19950 263.0000 C23 C25 C27
S England 1 0 Fortune, Mr. Charles Alexander male 19.0000 3 2 19950 263.0000 C23 C25 C27
S England 1 0 Fortune, Mr. Mark male 64.0000 1 4 19950 263.0000 C23 C25 C27
S England 1 1 Fortune, Mrs. Mark (Mary McDougald) female 60.0000 1 4 19950 263.0000 C23 C25 C27
C France 1 1 Francatelli, Miss. Laura Mabel female 30.0000 0 0 PC 17485 56.9292 E36
S England 1 0 Franklin, Mr. Thomas Parham male NA 0 0 113778 26.5500 D34
S England 1 1 Frauenthal, Dr. Henry William male 50.0000 2 0 PC 17611 133.6500 NA
C France 1 1 Frauenthal, Mr. Isaac Gerald male 43.0000 1 0 17765 27.7208 D40
S England 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female NA 1 0 PC 17611 133.6500 NA
C France 1 1 Frolicher, Miss. Hedwig Margaritha female 22.0000 0 2 13568 49.5000 B39
C France 1 1 Frolicher-Stehli, Mr. Maxmillian male 60.0000 1 1 13567 79.2000 B41
C France 1 1 Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli) female 48.0000 1 1 13567 79.2000 B41
S England 1 0 Fry, Mr. Richard male NA 0 0 112058 0.0000 B102
S England 1 0 Futrelle, Mr. Jacques Heath male 37.0000 1 0 113803 53.1000 C123
S England 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0000 1 0 113803 53.1000 C123
S England 1 0 Gee, Mr. Arthur H male 47.0000 0 0 111320 38.5000 E63
C France 1 1 Geiger, Miss. Amalie female 35.0000 0 0 113503 211.5000 C130
C France 1 1 Gibson, Miss. Dorothy Winifred female 22.0000 0 1 112378 59.4000 NA
C France 1 1 Gibson, Mrs. Leonard (Pauline C Boeson) female 45.0000 0 1 112378 59.4000 NA
C France 1 0 Giglio, Mr. Victor male 24.0000 0 0 PC 17593 79.2000 B86
C France 1 1 Goldenberg, Mr. Samuel L male 49.0000 1 0 17453 89.1042 C92
C France 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female NA 1 0 17453 89.1042 C92
C France 1 0 Goldschmidt, Mr. George B male 71.0000 0 0 PC 17754 34.6542 A5
C France 1 1 Gracie, Col. Archibald IV male 53.0000 0 0 113780 28.5000 C51
S England 1 1 Graham, Miss. Margaret Edith female 19.0000 0 0 112053 30.0000 B42
S England 1 0 Graham, Mr. George Edward male 38.0000 0 1 PC 17582 153.4625 C91
S England 1 1 Graham, Mrs. William Thompson (Edith Junkins) female 58.0000 0 1 PC 17582 153.4625 C125
C France 1 1 Greenfield, Mr. William Bertram male 23.0000 0 1 PC 17759 63.3583 D10 D12
C France 1 1 Greenfield, Mrs. Leo David (Blanche Strouse) female 45.0000 0 1 PC 17759 63.3583 D10 D12
C France 1 0 Guggenheim, Mr. Benjamin male 46.0000 0 0 PC 17593 79.2000 B82 B84
C France 1 1 Harder, Mr. George Achilles male 25.0000 1 0 11765 55.4417 E50
C France 1 1 Harder, Mrs. George Achilles (Dorothy Annan) female 25.0000 1 0 11765 55.4417 E50
C France 1 1 Harper, Mr. Henry Sleeper male 48.0000 1 0 PC 17572 76.7292 D33
C France 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0000 1 0 PC 17572 76.7292 D33
S England 1 0 Harrington, Mr. Charles H male NA 0 0 113796 42.4000 NA
S England 1 0 Harris, Mr. Henry Birkhardt male 45.0000 1 0 36973 83.4750 C83
S England 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) female 35.0000 1 0 36973 83.4750 C83
S England 1 0 Harrison, Mr. William male 40.0000 0 0 112059 0.0000 B94
C France 1 1 Hassab, Mr. Hammad male 27.0000 0 0 PC 17572 76.7292 D49
S England 1 1 Hawksford, Mr. Walter James male NA 0 0 16988 30.0000 D45
C France 1 1 Hays, Miss. Margaret Bechstein female 24.0000 0 0 11767 83.1583 C54
S England 1 0 Hays, Mr. Charles Melville male 55.0000 1 1 12749 93.5000 B69
S England 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52.0000 1 1 12749 93.5000 B69
S England 1 0 Head, Mr. Christopher male 42.0000 0 0 113038 42.5000 B11
S England 1 0 Hilliard, Mr. Herbert Henry male NA 0 0 17463 51.8625 E46
S England 1 0 Hipkins, Mr. William Edward male 55.0000 0 0 680 50.0000 C39
C France 1 1 Hippach, Miss. Jean Gertrude female 16.0000 0 1 111361 57.9792 B18
C France 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44.0000 0 1 111361 57.9792 B18
S England 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51.0000 1 0 13502 77.9583 D11
S England 1 0 Holverson, Mr. Alexander Oskar male 42.0000 1 0 113789 52.0000 NA
S England 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) female 35.0000 1 0 113789 52.0000 NA
C France 1 1 Homer, Mr. Harry ("Mr E Haven") male 35.0000 0 0 111426 26.5500 NA
S England 1 1 Hoyt, Mr. Frederick Maxfield male 38.0000 1 0 19943 90.0000 C93
C France 1 0 Hoyt, Mr. William Fisher male NA 0 0 PC 17600 30.6958 NA
S England 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35.0000 1 0 19943 90.0000 C93
NA Unknown 1 1 Icard, Miss. Amelie female 38.0000 0 0 113572 80.0000 B28
C France 1 0 Isham, Miss. Ann Elizabeth female 50.0000 0 0 PC 17595 28.7125 C49
S England 1 1 Ismay, Mr. Joseph Bruce male 49.0000 0 0 112058 0.0000 B52 B54 B56
S England 1 0 Jones, Mr. Charles Cresson male 46.0000 0 0 694 26.0000 NA
S England 1 0 Julian, Mr. Henry Forbes male 50.0000 0 0 113044 26.0000 E60
C France 1 0 Keeping, Mr. Edwin male 32.5000 0 0 113503 211.5000 C132
C France 1 0 Kent, Mr. Edward Austin male 58.0000 0 0 11771 29.7000 B37
S England 1 0 Kenyon, Mr. Frederick R male 41.0000 1 0 17464 51.8625 D21
S England 1 1 Kenyon, Mrs. Frederick R (Marion) female NA 1 0 17464 51.8625 D21
S England 1 1 Kimball, Mr. Edwin Nelson Jr male 42.0000 1 0 11753 52.5542 D19
S England 1 1 Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons) female 45.0000 1 0 11753 52.5542 D19
S England 1 0 Klaber, Mr. Herman male NA 0 0 113028 26.5500 C124
S England 1 1 Kreuchen, Miss. Emilie female 39.0000 0 0 24160 211.3375 NA
S England 1 1 Leader, Dr. Alice (Farnham) female 49.0000 0 0 17465 25.9292 D17
C France 1 1 LeRoy, Miss. Bertha female 30.0000 0 0 PC 17761 106.4250 NA
C France 1 1 Lesurer, Mr. Gustave J male 35.0000 0 0 PC 17755 512.3292 B101
C France 1 0 Lewy, Mr. Ervin G male NA 0 0 PC 17612 27.7208 NA
S England 1 0 Lindeberg-Lind, Mr. Erik Gustaf ("Mr Edward Lingrey") male 42.0000 0 0 17475 26.5500 NA
C France 1 1 Lindstrom, Mrs. Carl Johan (Sigrid Posse) female 55.0000 0 0 112377 27.7208 NA
S England 1 1 Lines, Miss. Mary Conover female 16.0000 0 1 PC 17592 39.4000 D28
S England 1 1 Lines, Mrs. Ernest H (Elizabeth Lindsey James) female 51.0000 0 1 PC 17592 39.4000 D28
S England 1 0 Long, Mr. Milton Clyde male 29.0000 0 0 113501 30.0000 D6
S England 1 1 Longley, Miss. Gretchen Fiske female 21.0000 0 0 13502 77.9583 D9
S England 1 0 Loring, Mr. Joseph Holland male 30.0000 0 0 113801 45.5000 NA
C France 1 1 Lurette, Miss. Elise female 58.0000 0 0 PC 17569 146.5208 B80
S England 1 1 Madill, Miss. Georgette Alexandra female 15.0000 0 1 24160 211.3375 B5
S England 1 0 Maguire, Mr. John Edward male 30.0000 0 0 110469 26.0000 C106
S England 1 1 Maioni, Miss. Roberta female 16.0000 0 0 110152 86.5000 B79
C France 1 1 Marechal, Mr. Pierre male NA 0 0 11774 29.7000 C47
S England 1 0 Marvin, Mr. Daniel Warner male 19.0000 1 0 113773 53.1000 D30
S England 1 1 Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson) female 18.0000 1 0 113773 53.1000 D30
C France 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24.0000 0 0 PC 17482 49.5042 C90
C France 1 0 McCaffry, Mr. Thomas Francis male 46.0000 0 0 13050 75.2417 C6
S England 1 0 McCarthy, Mr. Timothy J male 54.0000 0 0 17463 51.8625 E46
S England 1 1 McGough, Mr. James Robert male 36.0000 0 0 PC 17473 26.2875 E25
C France 1 0 Meyer, Mr. Edgar Joseph male 28.0000 1 0 PC 17604 82.1708 NA
C France 1 1 Meyer, Mrs. Edgar Joseph (Leila Saks) female NA 1 0 PC 17604 82.1708 NA
S England 1 0 Millet, Mr. Francis Davis male 65.0000 0 0 13509 26.5500 E38
Q Ireland 1 0 Minahan, Dr. William Edward male 44.0000 2 0 19928 90.0000 C78
Q Ireland 1 1 Minahan, Miss. Daisy E female 33.0000 1 0 19928 90.0000 C78
Q Ireland 1 1 Minahan, Mrs. William Edward (Lillian E Thorpe) female 37.0000 1 0 19928 90.0000 C78
C France 1 1 Mock, Mr. Philipp Edmund male 30.0000 1 0 13236 57.7500 C78
S England 1 0 Molson, Mr. Harry Markland male 55.0000 0 0 113787 30.5000 C30
S England 1 0 Moore, Mr. Clarence Bloomfield male 47.0000 0 0 113796 42.4000 NA
C France 1 0 Natsch, Mr. Charles H male 37.0000 0 1 PC 17596 29.7000 C118
C France 1 1 Newell, Miss. Madeleine female 31.0000 1 0 35273 113.2750 D36
C France 1 1 Newell, Miss. Marjorie female 23.0000 1 0 35273 113.2750 D36
C France 1 0 Newell, Mr. Arthur Webster male 58.0000 0 2 35273 113.2750 D48
S England 1 1 Newsom, Miss. Helen Monypeny female 19.0000 0 2 11752 26.2833 D47
S England 1 0 Nicholson, Mr. Arthur Ernest male 64.0000 0 0 693 26.0000 NA
C France 1 1 Oliva y Ocana, Dona. Fermina female 39.0000 0 0 PC 17758 108.9000 C105
C France 1 1 Omont, Mr. Alfred Fernand male NA 0 0 F.C. 12998 25.7417 NA
C France 1 1 Ostby, Miss. Helene Ragnhild female 22.0000 0 1 113509 61.9792 B36
C France 1 0 Ostby, Mr. Engelhart Cornelius male 65.0000 0 1 113509 61.9792 B30
C France 1 0 Ovies y Rodriguez, Mr. Servando male 28.5000 0 0 PC 17562 27.7208 D43
S England 1 0 Parr, Mr. William Henry Marsh male NA 0 0 112052 0.0000 NA
S England 1 0 Partner, Mr. Austen male 45.5000 0 0 113043 28.5000 C124
S England 1 0 Payne, Mr. Vivian Ponsonby male 23.0000 0 0 12749 93.5000 B24
S England 1 0 Pears, Mr. Thomas Clinton male 29.0000 1 0 113776 66.6000 C2
S England 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22.0000 1 0 113776 66.6000 C2
C France 1 0 Penasco y Castellana, Mr. Victor de Satode male 18.0000 1 0 PC 17758 108.9000 C65
C France 1 1 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) female 17.0000 1 0 PC 17758 108.9000 C65
S England 1 1 Perreault, Miss. Anne female 30.0000 0 0 12749 93.5000 B73
S England 1 1 Peuchen, Major. Arthur Godfrey male 52.0000 0 0 113786 30.5000 C104
S England 1 0 Porter, Mr. Walter Chamberlain male 47.0000 0 0 110465 52.0000 C110
C France 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0000 0 1 11767 83.1583 C50
S England 1 0 Reuchlin, Jonkheer. John George male 38.0000 0 0 19972 0.0000 NA
S England 1 1 Rheims, Mr. George Alexander Lucien male NA 0 0 PC 17607 39.6000 NA
C France 1 0 Ringhini, Mr. Sante male 22.0000 0 0 PC 17760 135.6333 NA
C France 1 0 Robbins, Mr. Victor male NA 0 0 PC 17757 227.5250 NA
S England 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43.0000 0 1 24160 211.3375 B3
S England 1 0 Roebling, Mr. Washington Augustus II male 31.0000 0 0 PC 17590 50.4958 A24
S England 1 1 Romaine, Mr. Charles Hallace ("Mr C Rolmane") male 45.0000 0 0 111428 26.5500 NA
S England 1 0 Rood, Mr. Hugh Roscoe male NA 0 0 113767 50.0000 A32
C France 1 1 Rosenbaum, Miss. Edith Louise female 33.0000 0 0 PC 17613 27.7208 A11
C France 1 0 Rosenshine, Mr. George ("Mr George Thorne") male 46.0000 0 0 PC 17585 79.2000 NA
C France 1 0 Ross, Mr. John Hugo male 36.0000 0 0 13049 40.1250 A10
S England 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33.0000 0 0 110152 86.5000 B77
C France 1 0 Rothschild, Mr. Martin male 55.0000 1 0 PC 17603 59.4000 NA
C France 1 1 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54.0000 1 0 PC 17603 59.4000 NA
S England 1 0 Rowe, Mr. Alfred G male 33.0000 0 0 113790 26.5500 NA
C France 1 1 Ryerson, Master. John Borie male 13.0000 2 2 PC 17608 262.3750 B57 B59 B63 B66
C France 1 1 Ryerson, Miss. Emily Borie female 18.0000 2 2 PC 17608 262.3750 B57 B59 B63 B66
C France 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21.0000 2 2 PC 17608 262.3750 B57 B59 B63 B66
C France 1 0 Ryerson, Mr. Arthur Larned male 61.0000 1 3 PC 17608 262.3750 B57 B59 B63 B66
C France 1 1 Ryerson, Mrs. Arthur Larned (Emily Maria Borie) female 48.0000 1 3 PC 17608 262.3750 B57 B59 B63 B66
S England 1 1 Saalfeld, Mr. Adolphe male NA 0 0 19988 30.5000 C106
C France 1 1 Sagesser, Mlle. Emma female 24.0000 0 0 PC 17477 69.3000 B35
S England 1 1 Salomon, Mr. Abraham L male NA 0 0 111163 26.0000 NA
C France 1 1 Schabert, Mrs. Paul (Emma Mock) female 35.0000 1 0 13236 57.7500 C28
C France 1 1 Serepeca, Miss. Augusta female 30.0000 0 0 113798 31.0000 NA
S England 1 1 Seward, Mr. Frederic Kimber male 34.0000 0 0 113794 26.5500 NA
S England 1 1 Shutes, Miss. Elizabeth W female 40.0000 0 0 PC 17582 153.4625 C125
S England 1 1 Silverthorne, Mr. Spencer Victor male 35.0000 0 0 PC 17475 26.2875 E24
S England 1 0 Silvey, Mr. William Baird male 50.0000 1 0 13507 55.9000 E44
S England 1 1 Silvey, Mrs. William Baird (Alice Munger) female 39.0000 1 0 13507 55.9000 E44
C France 1 1 Simonius-Blumer, Col. Oberst Alfons male 56.0000 0 0 13213 35.5000 A26
S England 1 1 Sloper, Mr. William Thompson male 28.0000 0 0 113788 35.5000 A6
S England 1 0 Smart, Mr. John Montgomery male 56.0000 0 0 113792 26.5500 NA
C France 1 0 Smith, Mr. James Clinch male 56.0000 0 0 17764 30.6958 A7
S England 1 0 Smith, Mr. Lucien Philip male 24.0000 1 0 13695 60.0000 C31
S England 1 0 Smith, Mr. Richard William male NA 0 0 113056 26.0000 A19
S England 1 1 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) female 18.0000 1 0 13695 60.0000 C31
S England 1 1 Snyder, Mr. John Pillsbury male 24.0000 1 0 21228 82.2667 B45
S England 1 1 Snyder, Mrs. John Pillsbury (Nelle Stevenson) female 23.0000 1 0 21228 82.2667 B45
C France 1 1 Spedden, Master. Robert Douglas male 6.0000 0 2 16966 134.5000 E34
C France 1 1 Spedden, Mr. Frederic Oakley male 45.0000 1 1 16966 134.5000 E34
C France 1 1 Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) female 40.0000 1 1 16966 134.5000 E34
C France 1 0 Spencer, Mr. William Augustus male 57.0000 1 0 PC 17569 146.5208 B78
C France 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 146.5208 B78
C France 1 1 Stahelin-Maeglin, Dr. Max male 32.0000 0 0 13214 30.5000 B50
S England 1 0 Stead, Mr. William Thomas male 62.0000 0 0 113514 26.5500 C87
C France 1 1 Stengel, Mr. Charles Emil Henry male 54.0000 1 0 11778 55.4417 C116
C France 1 1 Stengel, Mrs. Charles Emil Henry (Annie May Morris) female 43.0000 1 0 11778 55.4417 C116
C France 1 1 Stephenson, Mrs. Walter Bertram (Martha Eustis) female 52.0000 1 0 36947 78.2667 D20
C France 1 0 Stewart, Mr. Albert A male NA 0 0 PC 17605 27.7208 NA
NA Unknown 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62.0000 0 0 113572 80.0000 B28
S England 1 0 Straus, Mr. Isidor male 67.0000 1 0 PC 17483 221.7792 C55 C57
S England 1 0 Straus, Mrs. Isidor (Rosalie Ida Blun) female 63.0000 1 0 PC 17483 221.7792 C55 C57
S England 1 0 Sutton, Mr. Frederick male 61.0000 0 0 36963 32.3208 D50
S England 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48.0000 0 0 17466 25.9292 D17
S England 1 1 Taussig, Miss. Ruth female 18.0000 0 2 110413 79.6500 E68
S England 1 0 Taussig, Mr. Emil male 52.0000 1 1 110413 79.6500 E67
S England 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39.0000 1 1 110413 79.6500 E67
S England 1 1 Taylor, Mr. Elmer Zebley male 48.0000 1 0 19996 52.0000 C126
S England 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female NA 1 0 19996 52.0000 C126
C France 1 0 Thayer, Mr. John Borland male 49.0000 1 1 17421 110.8833 C68
C France 1 1 Thayer, Mr. John Borland Jr male 17.0000 0 2 17421 110.8833 C70
C France 1 1 Thayer, Mrs. John Borland (Marian Longstreth Morris) female 39.0000 1 1 17421 110.8833 C68
C France 1 1 Thorne, Mrs. Gertrude Maybelle female NA 0 0 PC 17585 79.2000 NA
C France 1 1 Tucker, Mr. Gilbert Milligan Jr male 31.0000 0 0 2543 28.5375 C53
C France 1 0 Uruchurtu, Don. Manuel E male 40.0000 0 0 PC 17601 27.7208 NA
S England 1 0 Van der hoef, Mr. Wyckoff male 61.0000 0 0 111240 33.5000 B19
S England 1 0 Walker, Mr. William Anderson male 47.0000 0 0 36967 34.0208 D46
C France 1 1 Ward, Miss. Anna female 35.0000 0 0 PC 17755 512.3292 NA
C France 1 0 Warren, Mr. Frank Manley male 64.0000 1 0 110813 75.2500 D37
C France 1 1 Warren, Mrs. Frank Manley (Anna Sophia Atkinson) female 60.0000 1 0 110813 75.2500 D37
S England 1 0 Weir, Col. John male 60.0000 0 0 113800 26.5500 NA
S England 1 0 White, Mr. Percival Wayland male 54.0000 0 1 35281 77.2875 D26
S England 1 0 White, Mr. Richard Frasar male 21.0000 0 1 35281 77.2875 D26
C France 1 1 White, Mrs. John Stuart (Ella Holmes) female 55.0000 0 0 PC 17760 135.6333 C32
S England 1 1 Wick, Miss. Mary Natalie female 31.0000 0 2 36928 164.8667 C7
S England 1 0 Wick, Mr. George Dennick male 57.0000 1 1 36928 164.8667 NA
S England 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.0000 1 1 36928 164.8667 NA
C France 1 0 Widener, Mr. George Dunton male 50.0000 1 1 113503 211.5000 C80
C France 1 0 Widener, Mr. Harry Elkins male 27.0000 0 2 113503 211.5000 C82
C France 1 1 Widener, Mrs. George Dunton (Eleanor Elkins) female 50.0000 1 1 113503 211.5000 C80
S England 1 1 Willard, Miss. Constance female 21.0000 0 0 113795 26.5500 NA
C France 1 0 Williams, Mr. Charles Duane male 51.0000 0 1 PC 17597 61.3792 NA
C France 1 1 Williams, Mr. Richard Norris II male 21.0000 0 1 PC 17597 61.3792 NA
S England 1 0 Williams-Lambert, Mr. Fletcher Fellows male NA 0 0 113510 35.0000 C128
C France 1 1 Wilson, Miss. Helen Alice female 31.0000 0 0 16966 134.5000 E39 E41
S England 1 1 Woolner, Mr. Hugh male NA 0 0 19947 35.5000 C52
S England 1 0 Wright, Mr. George male 62.0000 0 0 113807 26.5500 NA
C France 1 1 Young, Miss. Marie Grice female 36.0000 0 0 PC 17760 135.6333 C32
C France 2 0 Abelson, Mr. Samuel male 30.0000 1 0 P/PP 3381 24.0000 NA
C France 2 1 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0000 1 0 P/PP 3381 24.0000 NA
S England 2 0 Aldworth, Mr. Charles Augustus male 30.0000 0 0 248744 13.0000 NA
S England 2 0 Andrew, Mr. Edgardo Samuel male 18.0000 0 0 231945 11.5000 NA
S England 2 0 Andrew, Mr. Frank Thomas male 25.0000 0 0 C.A. 34050 10.5000 NA
S England 2 0 Angle, Mr. William A male 34.0000 1 0 226875 26.0000 NA
S England 2 1 Angle, Mrs. William A (Florence "Mary" Agnes Hughes) female 36.0000 1 0 226875 26.0000 NA
S England 2 0 Ashby, Mr. John male 57.0000 0 0 244346 13.0000 NA
S England 2 0 Bailey, Mr. Percy Andrew male 18.0000 0 0 29108 11.5000 NA
S England 2 0 Baimbrigge, Mr. Charles Robert male 23.0000 0 0 C.A. 31030 10.5000 NA
S England 2 1 Ball, Mrs. (Ada E Hall) female 36.0000 0 0 28551 13.0000 D
S England 2 0 Banfield, Mr. Frederick James male 28.0000 0 0 C.A./SOTON 34068 10.5000 NA
S England 2 0 Bateman, Rev. Robert James male 51.0000 0 0 S.O.P. 1166 12.5250 NA
S England 2 1 Beane, Mr. Edward male 32.0000 1 0 2908 26.0000 NA
S England 2 1 Beane, Mrs. Edward (Ethel Clarke) female 19.0000 1 0 2908 26.0000 NA
S England 2 0 Beauchamp, Mr. Henry James male 28.0000 0 0 244358 26.0000 NA
S England 2 1 Becker, Master. Richard F male 1.0000 2 1 230136 39.0000 F4
S England 2 1 Becker, Miss. Marion Louise female 4.0000 2 1 230136 39.0000 F4
S England 2 1 Becker, Miss. Ruth Elizabeth female 12.0000 2 1 230136 39.0000 F4
S England 2 1 Becker, Mrs. Allen Oliver (Nellie E Baumgardner) female 36.0000 0 3 230136 39.0000 F4
S England 2 1 Beesley, Mr. Lawrence male 34.0000 0 0 248698 13.0000 D56
S England 2 1 Bentham, Miss. Lilian W female 19.0000 0 0 28404 13.0000 NA
S England 2 0 Berriman, Mr. William John male 23.0000 0 0 28425 13.0000 NA
S England 2 0 Botsford, Mr. William Hull male 26.0000 0 0 237670 13.0000 NA
S England 2 0 Bowenur, Mr. Solomon male 42.0000 0 0 211535 13.0000 NA
S England 2 0 Bracken, Mr. James H male 27.0000 0 0 220367 13.0000 NA
S England 2 1 Brown, Miss. Amelia "Mildred" female 24.0000 0 0 248733 13.0000 F33
S England 2 1 Brown, Miss. Edith Eileen female 15.0000 0 2 29750 39.0000 NA
S England 2 0 Brown, Mr. Thomas William Solomon male 60.0000 1 1 29750 39.0000 NA
S England 2 1 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40.0000 1 1 29750 39.0000 NA
S England 2 1 Bryhl, Miss. Dagmar Jenny Ingeborg female 20.0000 1 0 236853 26.0000 NA
S England 2 0 Bryhl, Mr. Kurt Arnold Gottfrid male 25.0000 1 0 236853 26.0000 NA
S England 2 1 Buss, Miss. Kate female 36.0000 0 0 27849 13.0000 NA
S England 2 0 Butler, Mr. Reginald Fenton male 25.0000 0 0 234686 13.0000 NA
S England 2 0 Byles, Rev. Thomas Roussel Davids male 42.0000 0 0 244310 13.0000 NA
S England 2 1 Bystrom, Mrs. (Karolina) female 42.0000 0 0 236852 13.0000 NA
S England 2 1 Caldwell, Master. Alden Gates male 0.8333 0 2 248738 29.0000 NA
S England 2 1 Caldwell, Mr. Albert Francis male 26.0000 1 1 248738 29.0000 NA
S England 2 1 Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) female 22.0000 1 1 248738 29.0000 NA
S England 2 1 Cameron, Miss. Clear Annie female 35.0000 0 0 F.C.C. 13528 21.0000 NA
S England 2 0 Campbell, Mr. William male NA 0 0 239853 0.0000 NA
S England 2 0 Carbines, Mr. William male 19.0000 0 0 28424 13.0000 NA
S England 2 0 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44.0000 1 0 244252 26.0000 NA
S England 2 0 Carter, Rev. Ernest Courtenay male 54.0000 1 0 244252 26.0000 NA
S England 2 0 Chapman, Mr. Charles Henry male 52.0000 0 0 248731 13.5000 NA
S England 2 0 Chapman, Mr. John Henry male 37.0000 1 0 SC/AH 29037 26.0000 NA
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C France 3 1 Baclini, Miss. Helene Barbara female 0.7500 2 1 2666 19.2583 NA
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C France 3 0 Chronopoulos, Mr. Demetrios male 18.0000 1 0 2680 14.4542 NA
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S England 3 0 Strandberg, Miss. Ida Sofia female 22.0000 0 0 7553 9.8375 NA
S England 3 1 Stranden, Mr. Juho male 31.0000 0 0 STON/O 2. 3101288 7.9250 NA
S England 3 0 Strilic, Mr. Ivan male 27.0000 0 0 315083 8.6625 NA
S England 3 0 Strom, Miss. Telma Matilda female 2.0000 0 1 347054 10.4625 G6
S England 3 0 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29.0000 1 1 347054 10.4625 G6
S England 3 1 Sunderland, Mr. Victor Francis male 16.0000 0 0 SOTON/OQ 392089 8.0500 NA
S England 3 1 Sundman, Mr. Johan Julian male 44.0000 0 0 STON/O 2. 3101269 7.9250 NA
S England 3 0 Sutehall, Mr. Henry Jr male 25.0000 0 0 SOTON/OQ 392076 7.0500 NA
S England 3 0 Svensson, Mr. Johan male 74.0000 0 0 347060 7.7750 NA
S England 3 1 Svensson, Mr. Johan Cervin male 14.0000 0 0 7538 9.2250 NA
S England 3 0 Svensson, Mr. Olof male 24.0000 0 0 350035 7.7958 NA
S England 3 1 Tenglin, Mr. Gunnar Isidor male 25.0000 0 0 350033 7.7958 NA
S England 3 0 Theobald, Mr. Thomas Leonard male 34.0000 0 0 363294 8.0500 NA
C France 3 1 Thomas, Master. Assad Alexander male 0.4167 0 1 2625 8.5167 NA
C France 3 0 Thomas, Mr. Charles P male NA 1 0 2621 6.4375 NA
C France 3 0 Thomas, Mr. John male NA 0 0 2681 6.4375 NA
C France 3 0 Thomas, Mr. Tannous male NA 0 0 2684 7.2250 NA
C France 3 1 Thomas, Mrs. Alexander (Thamine "Thelma") female 16.0000 1 1 2625 8.5167 NA
S England 3 0 Thomson, Mr. Alexander Morrison male NA 0 0 32302 8.0500 NA
S England 3 0 Thorneycroft, Mr. Percival male NA 1 0 376564 16.1000 NA
S England 3 1 Thorneycroft, Mrs. Percival (Florence Kate White) female NA 1 0 376564 16.1000 NA
S England 3 0 Tikkanen, Mr. Juho male 32.0000 0 0 STON/O 2. 3101293 7.9250 NA
Q Ireland 3 0 Tobin, Mr. Roger male NA 0 0 383121 7.7500 F38
S England 3 0 Todoroff, Mr. Lalio male NA 0 0 349216 7.8958 NA
S England 3 0 Tomlin, Mr. Ernest Portage male 30.5000 0 0 364499 8.0500 NA
S England 3 0 Torber, Mr. Ernst William male 44.0000 0 0 364511 8.0500 NA
C France 3 0 Torfa, Mr. Assad male NA 0 0 2673 7.2292 NA
S England 3 1 Tornquist, Mr. William Henry male 25.0000 0 0 LINE 0.0000 NA
C France 3 0 Toufik, Mr. Nakli male NA 0 0 2641 7.2292 NA
C France 3 1 Touma, Master. Georges Youssef male 7.0000 1 1 2650 15.2458 NA
C France 3 1 Touma, Miss. Maria Youssef female 9.0000 1 1 2650 15.2458 NA
C France 3 1 Touma, Mrs. Darwis (Hanne Youssef Razi) female 29.0000 0 2 2650 15.2458 NA
S England 3 0 Turcin, Mr. Stjepan male 36.0000 0 0 349247 7.8958 NA
S England 3 1 Turja, Miss. Anna Sofia female 18.0000 0 0 4138 9.8417 NA
S England 3 1 Turkula, Mrs. (Hedwig) female 63.0000 0 0 4134 9.5875 NA
S England 3 0 van Billiard, Master. James William male NA 1 1 A/5. 851 14.5000 NA
S England 3 0 van Billiard, Master. Walter John male 11.5000 1 1 A/5. 851 14.5000 NA
S England 3 0 van Billiard, Mr. Austin Blyler male 40.5000 0 2 A/5. 851 14.5000 NA
S England 3 0 Van Impe, Miss. Catharina female 10.0000 0 2 345773 24.1500 NA
S England 3 0 Van Impe, Mr. Jean Baptiste male 36.0000 1 1 345773 24.1500 NA
S England 3 0 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30.0000 1 1 345773 24.1500 NA
S England 3 0 van Melkebeke, Mr. Philemon male NA 0 0 345777 9.5000 NA
S England 3 0 Vande Velde, Mr. Johannes Joseph male 33.0000 0 0 345780 9.5000 NA
S England 3 0 Vande Walle, Mr. Nestor Cyriel male 28.0000 0 0 345770 9.5000 NA
S England 3 0 Vanden Steen, Mr. Leo Peter male 28.0000 0 0 345783 9.5000 NA
S England 3 0 Vander Cruyssen, Mr. Victor male 47.0000 0 0 345765 9.0000 NA
S England 3 0 Vander Planke, Miss. Augusta Maria female 18.0000 2 0 345764 18.0000 NA
S England 3 0 Vander Planke, Mr. Julius male 31.0000 3 0 345763 18.0000 NA
S England 3 0 Vander Planke, Mr. Leo Edmondus male 16.0000 2 0 345764 18.0000 NA
S England 3 0 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.0000 1 0 345763 18.0000 NA
C France 3 1 Vartanian, Mr. David male 22.0000 0 0 2658 7.2250 NA
S England 3 0 Vendel, Mr. Olof Edvin male 20.0000 0 0 350416 7.8542 NA
S England 3 0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0000 0 0 350406 7.8542 NA
S England 3 0 Vovk, Mr. Janko male 22.0000 0 0 349252 7.8958 NA
S England 3 0 Waelens, Mr. Achille male 22.0000 0 0 345767 9.0000 NA
S England 3 0 Ware, Mr. Frederick male NA 0 0 359309 8.0500 NA
S England 3 0 Warren, Mr. Charles William male NA 0 0 C.A. 49867 7.5500 NA
S England 3 0 Webber, Mr. James male NA 0 0 SOTON/OQ 3101316 8.0500 NA
S England 3 0 Wenzel, Mr. Linhart male 32.5000 0 0 345775 9.5000 NA
C France 3 1 Whabee, Mrs. George Joseph (Shawneene Abi-Saab) female 38.0000 0 0 2688 7.2292 NA
S England 3 0 Widegren, Mr. Carl/Charles Peter male 51.0000 0 0 347064 7.7500 NA
S England 3 0 Wiklund, Mr. Jakob Alfred male 18.0000 1 0 3101267 6.4958 NA
S England 3 0 Wiklund, Mr. Karl Johan male 21.0000 1 0 3101266 6.4958 NA
S England 3 1 Wilkes, Mrs. James (Ellen Needs) female 47.0000 1 0 363272 7.0000 NA
S England 3 0 Willer, Mr. Aaron ("Abi Weller") male NA 0 0 3410 8.7125 NA
S England 3 0 Willey, Mr. Edward male NA 0 0 S.O./P.P. 751 7.5500 NA
S England 3 0 Williams, Mr. Howard Hugh "Harry" male NA 0 0 A/5 2466 8.0500 NA
S England 3 0 Williams, Mr. Leslie male 28.5000 0 0 54636 16.1000 NA
S England 3 0 Windelov, Mr. Einar male 21.0000 0 0 SOTON/OQ 3101317 7.2500 NA
S England 3 0 Wirz, Mr. Albert male 27.0000 0 0 315154 8.6625 NA
S England 3 0 Wiseman, Mr. Phillippe male NA 0 0 A/4. 34244 7.2500 NA
S England 3 0 Wittevrongel, Mr. Camille male 36.0000 0 0 345771 9.5000 NA
C France 3 0 Yasbeck, Mr. Antoni male 27.0000 1 0 2659 14.4542 NA
C France 3 1 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0000 1 0 2659 14.4542 NA
C France 3 0 Youseff, Mr. Gerious male 45.5000 0 0 2628 7.2250 NA
C France 3 0 Yousif, Mr. Wazli male NA 0 0 2647 7.2250 NA
C France 3 0 Yousseff, Mr. Gerious male NA 0 0 2627 14.4583 NA
C France 3 0 Zabour, Miss. Hileni female 14.5000 1 0 2665 14.4542 NA
C France 3 0 Zabour, Miss. Thamine female NA 1 0 2665 14.4542 NA
C France 3 0 Zakarian, Mr. Mapriededer male 26.5000 0 0 2656 7.2250 NA
C France 3 0 Zakarian, Mr. Ortin male 27.0000 0 0 2670 7.2250 NA
S England 3 0 Zimmerman, Mr. Leo male 29.0000 0 0 315082 7.8750 NA

case_when() statements are two-sided formulas where the left-hand side is a logical condition and the right-hand side is the value to assign when that test is TRUE, with the two sides separated by a ~.

Values that are never matched by any of the logical test’s within the statement get a default replacement value: NA.

To keep these values from getting NAs, include a final catch-all test and replacement. This is done by putting TRUE on the left-hand side and the catch-all value on the right.

The left-hand side must evaluate to a logical vector.

The right-hand side does not need to be logical but it must evaluate to the same type of vector.

You could also use the recode function here - this is also covered in Introduction to R, Chapter 4.

4.4.1 Exercise: Case When

  1. Use the case_when() function to create a new column in the titanic dataset called fare_level.

This new column should have 5 levels total:

  • If fare is less than 100, fare_level should be “cheap”
  • Between 100 and 200: “middling”
  • Between 200 and 300: “expensive”
  • Greater than or equal to 300: “very expensive”
  • Otherwise : “unknown”

Assign your tibble to the variable titanic_fare_level.

# PLEASE NOTE: THIS IS NOT REAL CODE
# The example hint shows the structure of a case_when() 
# FOR MORE INFORMATION USE THE R HELP DOCUMENTATION
# ?casewhen


titanic_fare_level <- titanic %>% 
  mutate(
    fare_level = case_when(
      condition ~ action,
      condition ~ action,
      condition ~ action
      )
    )

# To display the Data

titanic_fare_level
# Using case_when
# Here I am using the between function from dplyr 
# To specify the ranges using dplyr's between function


titanic_fare_level <- titanic %>% 
  dplyr::mutate(
    fare_level = dplyr::case_when(
      fare < 100 ~ "cheap",
      fare >= 100 & fare < 200 ~ "middling",
      fare >= 200 & fare < 300 ~ "expensive",
      fare >= 300 ~ "very expensive",
      TRUE ~ "unknown")
    )
fare_level fare pclass survived name_of_passenger sex_of_passenger age_of_passenger sibsp parch ticket cabin embarked
expensive 211.3375 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 B5 S
middling 151.5500 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 C22 C26 S
middling 151.5500 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 C22 C26 S
middling 151.5500 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 C22 C26 S
middling 151.5500 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 C22 C26 S
cheap 26.5500 1 1 Anderson, Mr. Harry male 48.0000 0 0 19952 E12 S
cheap 77.9583 1 1 Andrews, Miss. Kornelia Theodosia female 63.0000 1 0 13502 D7 S
cheap 0.0000 1 0 Andrews, Mr. Thomas Jr male NA 0 0 112050 A36 S
cheap 51.4792 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female NA 2 0 11769 C101 S
cheap 49.5042 1 0 Artagaveytia, Mr. Ramon male NA 0 0 PC 17609 NA C
expensive 227.5250 1 0 Astor, Col. John Jacob male NA 1 0 PC 17757 C62 C64 C
expensive 227.5250 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0000 1 0 PC 17757 C62 C64 C
cheap 69.3000 1 1 Aubart, Mme. Leontine Pauline female 24.0000 0 0 PC 17477 B35 C
cheap 78.8500 1 1 Barber, Miss. Ellen "Nellie" female 26.0000 0 0 19877 NA S
cheap 30.0000 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0000 0 0 27042 A23 S
cheap 25.9250 1 0 Baumann, Mr. John D male NA 0 0 PC 17318 NA S
expensive 247.5208 1 0 Baxter, Mr. Quigg Edmond male 24.0000 0 1 PC 17558 B58 B60 C
expensive 247.5208 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0000 0 1 PC 17558 B58 B60 C
cheap 76.2917 1 1 Bazzani, Miss. Albina female 32.0000 0 0 11813 D15 C
cheap 75.2417 1 0 Beattie, Mr. Thomson male 36.0000 0 0 13050 C6 C
cheap 52.5542 1 1 Beckwith, Mr. Richard Leonard male 37.0000 1 1 11751 D35 S
cheap 52.5542 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0000 1 1 11751 D35 S
cheap 30.0000 1 1 Behr, Mr. Karl Howell male 26.0000 0 0 111369 C148 C
expensive 227.5250 1 1 Bidois, Miss. Rosalie female 42.0000 0 0 PC 17757 NA C
expensive 221.7792 1 1 Bird, Miss. Ellen female 29.0000 0 0 PC 17483 C97 S
cheap 26.0000 1 0 Birnbaum, Mr. Jakob male 25.0000 0 0 13905 NA C
cheap 91.0792 1 1 Bishop, Mr. Dickinson H male 25.0000 1 0 11967 B49 C
cheap 91.0792 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19.0000 1 0 11967 B49 C
middling 135.6333 1 1 Bissette, Miss. Amelia female 35.0000 0 0 PC 17760 C99 S
cheap 26.5500 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.0000 0 0 110564 C52 S
cheap 35.5000 1 0 Blackwell, Mr. Stephen Weart male 45.0000 0 0 113784 T S
cheap 31.0000 1 1 Blank, Mr. Henry male 40.0000 0 0 112277 A31 C
middling 164.8667 1 1 Bonnell, Miss. Caroline female 30.0000 0 0 36928 C7 S
cheap 26.5500 1 1 Bonnell, Miss. Elizabeth female 58.0000 0 0 113783 C103 S
cheap 26.5500 1 0 Borebank, Mr. John James male 42.0000 0 0 110489 D22 S
expensive 262.3750 1 1 Bowen, Miss. Grace Scott female 45.0000 0 0 PC 17608 NA C
cheap 55.0000 1 1 Bowerman, Miss. Elsie Edith female 22.0000 0 1 113505 E33 S
cheap 26.5500 1 1 Bradley, Mr. George ("George Arthur Brayton") male NA 0 0 111427 NA S
cheap 30.5000 1 0 Brady, Mr. John Bertram male 41.0000 0 0 113054 A21 S
cheap 50.4958 1 0 Brandeis, Mr. Emil male 48.0000 0 0 PC 17591 B10 C
cheap 39.6000 1 0 Brewe, Dr. Arthur Jackson male NA 0 0 112379 NA C
cheap 27.7208 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44.0000 0 0 PC 17610 B4 C
cheap 51.4792 1 1 Brown, Mrs. John Murray (Caroline Lane Lamson) female 59.0000 2 0 11769 C101 S
cheap 76.2917 1 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) female 60.0000 0 0 11813 D15 C
middling 134.5000 1 1 Burns, Miss. Elizabeth Margaret female 41.0000 0 0 16966 E40 C
cheap 26.5500 1 0 Butt, Major. Archibald Willingham male 45.0000 0 0 113050 B38 S
cheap 31.0000 1 0 Cairns, Mr. Alexander male NA 0 0 113798 NA S
cheap 26.2875 1 1 Calderhead, Mr. Edward Pennington male 42.0000 0 0 PC 17476 E24 S
cheap 27.4458 1 1 Candee, Mrs. Edward (Helen Churchill Hungerford) female 53.0000 0 0 PC 17606 NA C
very expensive 512.3292 1 1 Cardeza, Mr. Thomas Drake Martinez male 36.0000 0 1 PC 17755 B51 B53 B55 C
very expensive 512.3292 1 1 Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake) female 58.0000 0 1 PC 17755 B51 B53 B55 C
cheap 5.0000 1 0 Carlsson, Mr. Frans Olof male 33.0000 0 0 695 B51 B53 B55 S
cheap 47.1000 1 0 Carrau, Mr. Francisco M male 28.0000 0 0 113059 NA S
cheap 47.1000 1 0 Carrau, Mr. Jose Pedro male 17.0000 0 0 113059 NA S
middling 120.0000 1 1 Carter, Master. William Thornton II male 11.0000 1 2 113760 B96 B98 S
middling 120.0000 1 1 Carter, Miss. Lucile Polk female 14.0000 1 2 113760 B96 B98 S
middling 120.0000 1 1 Carter, Mr. William Ernest male 36.0000 1 2 113760 B96 B98 S
middling 120.0000 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36.0000 1 2 113760 B96 B98 S
cheap 26.0000 1 0 Case, Mr. Howard Brown male 49.0000 0 0 19924 NA S
cheap 27.7208 1 1 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) female NA 0 0 17770 NA C
cheap 78.8500 1 0 Cavendish, Mr. Tyrell William male 36.0000 1 0 19877 C46 S
cheap 78.8500 1 1 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) female 76.0000 1 0 19877 C46 S
cheap 61.1750 1 0 Chaffee, Mr. Herbert Fuller male 46.0000 1 0 W.E.P. 5734 E31 S
cheap 61.1750 1 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood) female 47.0000 1 0 W.E.P. 5734 E31 S
cheap 53.1000 1 1 Chambers, Mr. Norman Campbell male 27.0000 1 0 113806 E8 S
cheap 53.1000 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.0000 1 0 113806 E8 S
expensive 262.3750 1 1 Chaudanson, Miss. Victorine female 36.0000 0 0 PC 17608 B61 C
cheap 86.5000 1 1 Cherry, Miss. Gladys female 30.0000 0 0 110152 B77 S
cheap 29.7000 1 1 Chevre, Mr. Paul Romaine male 45.0000 0 0 PC 17594 A9 C
cheap 55.0000 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female NA 0 1 113505 E33 S
cheap 0.0000 1 0 Chisholm, Mr. Roderick Robert Crispin male NA 0 0 112051 NA S
middling 136.7792 1 0 Clark, Mr. Walter Miller male 27.0000 1 0 13508 C89 C
middling 136.7792 1 1 Clark, Mrs. Walter Miller (Virginia McDowell) female 26.0000 1 0 13508 C89 C
middling 151.5500 1 1 Cleaver, Miss. Alice female 22.0000 0 0 113781 NA S
cheap 52.0000 1 0 Clifford, Mr. George Quincy male NA 0 0 110465 A14 S
cheap 25.5875 1 0 Colley, Mr. Edward Pomeroy male 47.0000 0 0 5727 E58 S
cheap 83.1583 1 1 Compton, Miss. Sara Rebecca female 39.0000 1 1 PC 17756 E49 C
cheap 83.1583 1 0 Compton, Mr. Alexander Taylor Jr male 37.0000 1 1 PC 17756 E52 C
cheap 83.1583 1 1 Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) female 64.0000 0 2 PC 17756 E45 C
cheap 25.7000 1 1 Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) female 55.0000 2 0 11770 C101 S
cheap 26.5500 1 0 Crafton, Mr. John Bertram male NA 0 0 113791 NA S
cheap 71.0000 1 0 Crosby, Capt. Edward Gifford male 70.0000 1 1 WE/P 5735 B22 S
cheap 71.0000 1 1 Crosby, Miss. Harriet R female 36.0000 0 2 WE/P 5735 B22 S
cheap 26.5500 1 1 Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) female 64.0000 1 1 112901 B26 S
cheap 71.2833 1 0 Cumings, Mr. John Bradley male 39.0000 1 0 PC 17599 C85 C
cheap 71.2833 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0000 1 0 PC 17599 C85 C
cheap 26.5500 1 1 Daly, Mr. Peter Denis male 51.0000 0 0 113055 E17 S
cheap 30.5000 1 1 Daniel, Mr. Robert Williams male 27.0000 0 0 113804 NA S
middling 151.5500 1 1 Daniels, Miss. Sarah female 33.0000 0 0 113781 NA S
cheap 52.0000 1 0 Davidson, Mr. Thornton male 31.0000 1 0 F.C. 12750 B71 S
cheap 52.0000 1 1 Davidson, Mrs. Thornton (Orian Hays) female 27.0000 1 2 F.C. 12750 B71 S
cheap 57.0000 1 1 Dick, Mr. Albert Adrian male 31.0000 1 0 17474 B20 S
cheap 57.0000 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.0000 1 0 17474 B20 S
cheap 81.8583 1 1 Dodge, Dr. Washington male 53.0000 1 1 33638 A34 S
cheap 81.8583 1 1 Dodge, Master. Washington male 4.0000 0 2 33638 A34 S
cheap 81.8583 1 1 Dodge, Mrs. Washington (Ruth Vidaver) female 54.0000 1 1 33638 A34 S
middling 106.4250 1 0 Douglas, Mr. Walter Donald male 50.0000 1 0 PC 17761 C86 C
expensive 247.5208 1 1 Douglas, Mrs. Frederick Charles (Mary Helene Baxter) female 27.0000 1 1 PC 17558 B58 B60 C
middling 106.4250 1 1 Douglas, Mrs. Walter Donald (Mahala Dutton) female 48.0000 1 0 PC 17761 C86 C
cheap 39.6000 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") female 48.0000 1 0 11755 A16 C
cheap 56.9292 1 1 Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") male 49.0000 1 0 PC 17485 A20 C
cheap 29.7000 1 0 Dulles, Mr. William Crothers male 39.0000 0 0 PC 17580 A18 C
cheap 83.1583 1 1 Earnshaw, Mrs. Boulton (Olive Potter) female 23.0000 0 1 11767 C54 C
expensive 227.5250 1 1 Endres, Miss. Caroline Louise female 38.0000 0 0 PC 17757 C45 C
cheap 78.2667 1 1 Eustis, Miss. Elizabeth Mussey female 54.0000 1 0 36947 D20 C
cheap 31.6792 1 0 Evans, Miss. Edith Corse female 36.0000 0 0 PC 17531 A29 C
expensive 221.7792 1 0 Farthing, Mr. John male NA 0 0 PC 17483 C95 S
cheap 31.6833 1 1 Flegenheim, Mrs. Alfred (Antoinette) female NA 0 0 PC 17598 NA S
middling 110.8833 1 1 Fleming, Miss. Margaret female NA 0 0 17421 NA C
cheap 26.3875 1 1 Flynn, Mr. John Irwin ("Irving") male 36.0000 0 0 PC 17474 E25 S
cheap 27.7500 1 0 Foreman, Mr. Benjamin Laventall male 30.0000 0 0 113051 C111 C
expensive 263.0000 1 1 Fortune, Miss. Alice Elizabeth female 24.0000 3 2 19950 C23 C25 C27 S
expensive 263.0000 1 1 Fortune, Miss. Ethel Flora female 28.0000 3 2 19950 C23 C25 C27 S
expensive 263.0000 1 1 Fortune, Miss. Mabel Helen female 23.0000 3 2 19950 C23 C25 C27 S
expensive 263.0000 1 0 Fortune, Mr. Charles Alexander male 19.0000 3 2 19950 C23 C25 C27 S
expensive 263.0000 1 0 Fortune, Mr. Mark male 64.0000 1 4 19950 C23 C25 C27 S
expensive 263.0000 1 1 Fortune, Mrs. Mark (Mary McDougald) female 60.0000 1 4 19950 C23 C25 C27 S
cheap 56.9292 1 1 Francatelli, Miss. Laura Mabel female 30.0000 0 0 PC 17485 E36 C
cheap 26.5500 1 0 Franklin, Mr. Thomas Parham male NA 0 0 113778 D34 S
middling 133.6500 1 1 Frauenthal, Dr. Henry William male 50.0000 2 0 PC 17611 NA S
cheap 27.7208 1 1 Frauenthal, Mr. Isaac Gerald male 43.0000 1 0 17765 D40 C
middling 133.6500 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female NA 1 0 PC 17611 NA S
cheap 49.5000 1 1 Frolicher, Miss. Hedwig Margaritha female 22.0000 0 2 13568 B39 C
cheap 79.2000 1 1 Frolicher-Stehli, Mr. Maxmillian male 60.0000 1 1 13567 B41 C
cheap 79.2000 1 1 Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli) female 48.0000 1 1 13567 B41 C
cheap 0.0000 1 0 Fry, Mr. Richard male NA 0 0 112058 B102 S
cheap 53.1000 1 0 Futrelle, Mr. Jacques Heath male 37.0000 1 0 113803 C123 S
cheap 53.1000 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0000 1 0 113803 C123 S
cheap 38.5000 1 0 Gee, Mr. Arthur H male 47.0000 0 0 111320 E63 S
expensive 211.5000 1 1 Geiger, Miss. Amalie female 35.0000 0 0 113503 C130 C
cheap 59.4000 1 1 Gibson, Miss. Dorothy Winifred female 22.0000 0 1 112378 NA C
cheap 59.4000 1 1 Gibson, Mrs. Leonard (Pauline C Boeson) female 45.0000 0 1 112378 NA C
cheap 79.2000 1 0 Giglio, Mr. Victor male 24.0000 0 0 PC 17593 B86 C
cheap 89.1042 1 1 Goldenberg, Mr. Samuel L male 49.0000 1 0 17453 C92 C
cheap 89.1042 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female NA 1 0 17453 C92 C
cheap 34.6542 1 0 Goldschmidt, Mr. George B male 71.0000 0 0 PC 17754 A5 C
cheap 28.5000 1 1 Gracie, Col. Archibald IV male 53.0000 0 0 113780 C51 C
cheap 30.0000 1 1 Graham, Miss. Margaret Edith female 19.0000 0 0 112053 B42 S
middling 153.4625 1 0 Graham, Mr. George Edward male 38.0000 0 1 PC 17582 C91 S
middling 153.4625 1 1 Graham, Mrs. William Thompson (Edith Junkins) female 58.0000 0 1 PC 17582 C125 S
cheap 63.3583 1 1 Greenfield, Mr. William Bertram male 23.0000 0 1 PC 17759 D10 D12 C
cheap 63.3583 1 1 Greenfield, Mrs. Leo David (Blanche Strouse) female 45.0000 0 1 PC 17759 D10 D12 C
cheap 79.2000 1 0 Guggenheim, Mr. Benjamin male 46.0000 0 0 PC 17593 B82 B84 C
cheap 55.4417 1 1 Harder, Mr. George Achilles male 25.0000 1 0 11765 E50 C
cheap 55.4417 1 1 Harder, Mrs. George Achilles (Dorothy Annan) female 25.0000 1 0 11765 E50 C
cheap 76.7292 1 1 Harper, Mr. Henry Sleeper male 48.0000 1 0 PC 17572 D33 C
cheap 76.7292 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0000 1 0 PC 17572 D33 C
cheap 42.4000 1 0 Harrington, Mr. Charles H male NA 0 0 113796 NA S
cheap 83.4750 1 0 Harris, Mr. Henry Birkhardt male 45.0000 1 0 36973 C83 S
cheap 83.4750 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) female 35.0000 1 0 36973 C83 S
cheap 0.0000 1 0 Harrison, Mr. William male 40.0000 0 0 112059 B94 S
cheap 76.7292 1 1 Hassab, Mr. Hammad male 27.0000 0 0 PC 17572 D49 C
cheap 30.0000 1 1 Hawksford, Mr. Walter James male NA 0 0 16988 D45 S
cheap 83.1583 1 1 Hays, Miss. Margaret Bechstein female 24.0000 0 0 11767 C54 C
cheap 93.5000 1 0 Hays, Mr. Charles Melville male 55.0000 1 1 12749 B69 S
cheap 93.5000 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52.0000 1 1 12749 B69 S
cheap 42.5000 1 0 Head, Mr. Christopher male 42.0000 0 0 113038 B11 S
cheap 51.8625 1 0 Hilliard, Mr. Herbert Henry male NA 0 0 17463 E46 S
cheap 50.0000 1 0 Hipkins, Mr. William Edward male 55.0000 0 0 680 C39 S
cheap 57.9792 1 1 Hippach, Miss. Jean Gertrude female 16.0000 0 1 111361 B18 C
cheap 57.9792 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44.0000 0 1 111361 B18 C
cheap 77.9583 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51.0000 1 0 13502 D11 S
cheap 52.0000 1 0 Holverson, Mr. Alexander Oskar male 42.0000 1 0 113789 NA S
cheap 52.0000 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) female 35.0000 1 0 113789 NA S
cheap 26.5500 1 1 Homer, Mr. Harry ("Mr E Haven") male 35.0000 0 0 111426 NA C
cheap 90.0000 1 1 Hoyt, Mr. Frederick Maxfield male 38.0000 1 0 19943 C93 S
cheap 30.6958 1 0 Hoyt, Mr. William Fisher male NA 0 0 PC 17600 NA C
cheap 90.0000 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35.0000 1 0 19943 C93 S
cheap 80.0000 1 1 Icard, Miss. Amelie female 38.0000 0 0 113572 B28 NA
cheap 28.7125 1 0 Isham, Miss. Ann Elizabeth female 50.0000 0 0 PC 17595 C49 C
cheap 0.0000 1 1 Ismay, Mr. Joseph Bruce male 49.0000 0 0 112058 B52 B54 B56 S
cheap 26.0000 1 0 Jones, Mr. Charles Cresson male 46.0000 0 0 694 NA S
cheap 26.0000 1 0 Julian, Mr. Henry Forbes male 50.0000 0 0 113044 E60 S
expensive 211.5000 1 0 Keeping, Mr. Edwin male 32.5000 0 0 113503 C132 C
cheap 29.7000 1 0 Kent, Mr. Edward Austin male 58.0000 0 0 11771 B37 C
cheap 51.8625 1 0 Kenyon, Mr. Frederick R male 41.0000 1 0 17464 D21 S
cheap 51.8625 1 1 Kenyon, Mrs. Frederick R (Marion) female NA 1 0 17464 D21 S
cheap 52.5542 1 1 Kimball, Mr. Edwin Nelson Jr male 42.0000 1 0 11753 D19 S
cheap 52.5542 1 1 Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons) female 45.0000 1 0 11753 D19 S
cheap 26.5500 1 0 Klaber, Mr. Herman male NA 0 0 113028 C124 S
expensive 211.3375 1 1 Kreuchen, Miss. Emilie female 39.0000 0 0 24160 NA S
cheap 25.9292 1 1 Leader, Dr. Alice (Farnham) female 49.0000 0 0 17465 D17 S
middling 106.4250 1 1 LeRoy, Miss. Bertha female 30.0000 0 0 PC 17761 NA C
very expensive 512.3292 1 1 Lesurer, Mr. Gustave J male 35.0000 0 0 PC 17755 B101 C
cheap 27.7208 1 0 Lewy, Mr. Ervin G male NA 0 0 PC 17612 NA C
cheap 26.5500 1 0 Lindeberg-Lind, Mr. Erik Gustaf ("Mr Edward Lingrey") male 42.0000 0 0 17475 NA S
cheap 27.7208 1 1 Lindstrom, Mrs. Carl Johan (Sigrid Posse) female 55.0000 0 0 112377 NA C
cheap 39.4000 1 1 Lines, Miss. Mary Conover female 16.0000 0 1 PC 17592 D28 S
cheap 39.4000 1 1 Lines, Mrs. Ernest H (Elizabeth Lindsey James) female 51.0000 0 1 PC 17592 D28 S
cheap 30.0000 1 0 Long, Mr. Milton Clyde male 29.0000 0 0 113501 D6 S
cheap 77.9583 1 1 Longley, Miss. Gretchen Fiske female 21.0000 0 0 13502 D9 S
cheap 45.5000 1 0 Loring, Mr. Joseph Holland male 30.0000 0 0 113801 NA S
middling 146.5208 1 1 Lurette, Miss. Elise female 58.0000 0 0 PC 17569 B80 C
expensive 211.3375 1 1 Madill, Miss. Georgette Alexandra female 15.0000 0 1 24160 B5 S
cheap 26.0000 1 0 Maguire, Mr. John Edward male 30.0000 0 0 110469 C106 S
cheap 86.5000 1 1 Maioni, Miss. Roberta female 16.0000 0 0 110152 B79 S
cheap 29.7000 1 1 Marechal, Mr. Pierre male NA 0 0 11774 C47 C
cheap 53.1000 1 0 Marvin, Mr. Daniel Warner male 19.0000 1 0 113773 D30 S
cheap 53.1000 1 1 Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson) female 18.0000 1 0 113773 D30 S
cheap 49.5042 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24.0000 0 0 PC 17482 C90 C
cheap 75.2417 1 0 McCaffry, Mr. Thomas Francis male 46.0000 0 0 13050 C6 C
cheap 51.8625 1 0 McCarthy, Mr. Timothy J male 54.0000 0 0 17463 E46 S
cheap 26.2875 1 1 McGough, Mr. James Robert male 36.0000 0 0 PC 17473 E25 S
cheap 82.1708 1 0 Meyer, Mr. Edgar Joseph male 28.0000 1 0 PC 17604 NA C
cheap 82.1708 1 1 Meyer, Mrs. Edgar Joseph (Leila Saks) female NA 1 0 PC 17604 NA C
cheap 26.5500 1 0 Millet, Mr. Francis Davis male 65.0000 0 0 13509 E38 S
cheap 90.0000 1 0 Minahan, Dr. William Edward male 44.0000 2 0 19928 C78 Q
cheap 90.0000 1 1 Minahan, Miss. Daisy E female 33.0000 1 0 19928 C78 Q
cheap 90.0000 1 1 Minahan, Mrs. William Edward (Lillian E Thorpe) female 37.0000 1 0 19928 C78 Q
cheap 57.7500 1 1 Mock, Mr. Philipp Edmund male 30.0000 1 0 13236 C78 C
cheap 30.5000 1 0 Molson, Mr. Harry Markland male 55.0000 0 0 113787 C30 S
cheap 42.4000 1 0 Moore, Mr. Clarence Bloomfield male 47.0000 0 0 113796 NA S
cheap 29.7000 1 0 Natsch, Mr. Charles H male 37.0000 0 1 PC 17596 C118 C
middling 113.2750 1 1 Newell, Miss. Madeleine female 31.0000 1 0 35273 D36 C
middling 113.2750 1 1 Newell, Miss. Marjorie female 23.0000 1 0 35273 D36 C
middling 113.2750 1 0 Newell, Mr. Arthur Webster male 58.0000 0 2 35273 D48 C
cheap 26.2833 1 1 Newsom, Miss. Helen Monypeny female 19.0000 0 2 11752 D47 S
cheap 26.0000 1 0 Nicholson, Mr. Arthur Ernest male 64.0000 0 0 693 NA S
middling 108.9000 1 1 Oliva y Ocana, Dona. Fermina female 39.0000 0 0 PC 17758 C105 C
cheap 25.7417 1 1 Omont, Mr. Alfred Fernand male NA 0 0 F.C. 12998 NA C
cheap 61.9792 1 1 Ostby, Miss. Helene Ragnhild female 22.0000 0 1 113509 B36 C
cheap 61.9792 1 0 Ostby, Mr. Engelhart Cornelius male 65.0000 0 1 113509 B30 C
cheap 27.7208 1 0 Ovies y Rodriguez, Mr. Servando male 28.5000 0 0 PC 17562 D43 C
cheap 0.0000 1 0 Parr, Mr. William Henry Marsh male NA 0 0 112052 NA S
cheap 28.5000 1 0 Partner, Mr. Austen male 45.5000 0 0 113043 C124 S
cheap 93.5000 1 0 Payne, Mr. Vivian Ponsonby male 23.0000 0 0 12749 B24 S
cheap 66.6000 1 0 Pears, Mr. Thomas Clinton male 29.0000 1 0 113776 C2 S
cheap 66.6000 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22.0000 1 0 113776 C2 S
middling 108.9000 1 0 Penasco y Castellana, Mr. Victor de Satode male 18.0000 1 0 PC 17758 C65 C
middling 108.9000 1 1 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) female 17.0000 1 0 PC 17758 C65 C
cheap 93.5000 1 1 Perreault, Miss. Anne female 30.0000 0 0 12749 B73 S
cheap 30.5000 1 1 Peuchen, Major. Arthur Godfrey male 52.0000 0 0 113786 C104 S
cheap 52.0000 1 0 Porter, Mr. Walter Chamberlain male 47.0000 0 0 110465 C110 S
cheap 83.1583 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0000 0 1 11767 C50 C
cheap 0.0000 1 0 Reuchlin, Jonkheer. John George male 38.0000 0 0 19972 NA S
cheap 39.6000 1 1 Rheims, Mr. George Alexander Lucien male NA 0 0 PC 17607 NA S
middling 135.6333 1 0 Ringhini, Mr. Sante male 22.0000 0 0 PC 17760 NA C
expensive 227.5250 1 0 Robbins, Mr. Victor male NA 0 0 PC 17757 NA C
expensive 211.3375 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43.0000 0 1 24160 B3 S
cheap 50.4958 1 0 Roebling, Mr. Washington Augustus II male 31.0000 0 0 PC 17590 A24 S
cheap 26.5500 1 1 Romaine, Mr. Charles Hallace ("Mr C Rolmane") male 45.0000 0 0 111428 NA S
cheap 50.0000 1 0 Rood, Mr. Hugh Roscoe male NA 0 0 113767 A32 S
cheap 27.7208 1 1 Rosenbaum, Miss. Edith Louise female 33.0000 0 0 PC 17613 A11 C
cheap 79.2000 1 0 Rosenshine, Mr. George ("Mr George Thorne") male 46.0000 0 0 PC 17585 NA C
cheap 40.1250 1 0 Ross, Mr. John Hugo male 36.0000 0 0 13049 A10 C
cheap 86.5000 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33.0000 0 0 110152 B77 S
cheap 59.4000 1 0 Rothschild, Mr. Martin male 55.0000 1 0 PC 17603 NA C
cheap 59.4000 1 1 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54.0000 1 0 PC 17603 NA C
cheap 26.5500 1 0 Rowe, Mr. Alfred G male 33.0000 0 0 113790 NA S
expensive 262.3750 1 1 Ryerson, Master. John Borie male 13.0000 2 2 PC 17608 B57 B59 B63 B66 C
expensive 262.3750 1 1 Ryerson, Miss. Emily Borie female 18.0000 2 2 PC 17608 B57 B59 B63 B66 C
expensive 262.3750 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21.0000 2 2 PC 17608 B57 B59 B63 B66 C
expensive 262.3750 1 0 Ryerson, Mr. Arthur Larned male 61.0000 1 3 PC 17608 B57 B59 B63 B66 C
expensive 262.3750 1 1 Ryerson, Mrs. Arthur Larned (Emily Maria Borie) female 48.0000 1 3 PC 17608 B57 B59 B63 B66 C
cheap 30.5000 1 1 Saalfeld, Mr. Adolphe male NA 0 0 19988 C106 S
cheap 69.3000 1 1 Sagesser, Mlle. Emma female 24.0000 0 0 PC 17477 B35 C
cheap 26.0000 1 1 Salomon, Mr. Abraham L male NA 0 0 111163 NA S
cheap 57.7500 1 1 Schabert, Mrs. Paul (Emma Mock) female 35.0000 1 0 13236 C28 C
cheap 31.0000 1 1 Serepeca, Miss. Augusta female 30.0000 0 0 113798 NA C
cheap 26.5500 1 1 Seward, Mr. Frederic Kimber male 34.0000 0 0 113794 NA S
middling 153.4625 1 1 Shutes, Miss. Elizabeth W female 40.0000 0 0 PC 17582 C125 S
cheap 26.2875 1 1 Silverthorne, Mr. Spencer Victor male 35.0000 0 0 PC 17475 E24 S
cheap 55.9000 1 0 Silvey, Mr. William Baird male 50.0000 1 0 13507 E44 S
cheap 55.9000 1 1 Silvey, Mrs. William Baird (Alice Munger) female 39.0000 1 0 13507 E44 S
cheap 35.5000 1 1 Simonius-Blumer, Col. Oberst Alfons male 56.0000 0 0 13213 A26 C
cheap 35.5000 1 1 Sloper, Mr. William Thompson male 28.0000 0 0 113788 A6 S
cheap 26.5500 1 0 Smart, Mr. John Montgomery male 56.0000 0 0 113792 NA S
cheap 30.6958 1 0 Smith, Mr. James Clinch male 56.0000 0 0 17764 A7 C
cheap 60.0000 1 0 Smith, Mr. Lucien Philip male 24.0000 1 0 13695 C31 S
cheap 26.0000 1 0 Smith, Mr. Richard William male NA 0 0 113056 A19 S
cheap 60.0000 1 1 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) female 18.0000 1 0 13695 C31 S
cheap 82.2667 1 1 Snyder, Mr. John Pillsbury male 24.0000 1 0 21228 B45 S
cheap 82.2667 1 1 Snyder, Mrs. John Pillsbury (Nelle Stevenson) female 23.0000 1 0 21228 B45 S
middling 134.5000 1 1 Spedden, Master. Robert Douglas male 6.0000 0 2 16966 E34 C
middling 134.5000 1 1 Spedden, Mr. Frederic Oakley male 45.0000 1 1 16966 E34 C
middling 134.5000 1 1 Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) female 40.0000 1 1 16966 E34 C
middling 146.5208 1 0 Spencer, Mr. William Augustus male 57.0000 1 0 PC 17569 B78 C
middling 146.5208 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 B78 C
cheap 30.5000 1 1 Stahelin-Maeglin, Dr. Max male 32.0000 0 0 13214 B50 C
cheap 26.5500 1 0 Stead, Mr. William Thomas male 62.0000 0 0 113514 C87 S
cheap 55.4417 1 1 Stengel, Mr. Charles Emil Henry male 54.0000 1 0 11778 C116 C
cheap 55.4417 1 1 Stengel, Mrs. Charles Emil Henry (Annie May Morris) female 43.0000 1 0 11778 C116 C
cheap 78.2667 1 1 Stephenson, Mrs. Walter Bertram (Martha Eustis) female 52.0000 1 0 36947 D20 C
cheap 27.7208 1 0 Stewart, Mr. Albert A male NA 0 0 PC 17605 NA C
cheap 80.0000 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62.0000 0 0 113572 B28 NA
expensive 221.7792 1 0 Straus, Mr. Isidor male 67.0000 1 0 PC 17483 C55 C57 S
expensive 221.7792 1 0 Straus, Mrs. Isidor (Rosalie Ida Blun) female 63.0000 1 0 PC 17483 C55 C57 S
cheap 32.3208 1 0 Sutton, Mr. Frederick male 61.0000 0 0 36963 D50 S
cheap 25.9292 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48.0000 0 0 17466 D17 S
cheap 79.6500 1 1 Taussig, Miss. Ruth female 18.0000 0 2 110413 E68 S
cheap 79.6500 1 0 Taussig, Mr. Emil male 52.0000 1 1 110413 E67 S
cheap 79.6500 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39.0000 1 1 110413 E67 S
cheap 52.0000 1 1 Taylor, Mr. Elmer Zebley male 48.0000 1 0 19996 C126 S
cheap 52.0000 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female NA 1 0 19996 C126 S
middling 110.8833 1 0 Thayer, Mr. John Borland male 49.0000 1 1 17421 C68 C
middling 110.8833 1 1 Thayer, Mr. John Borland Jr male 17.0000 0 2 17421 C70 C
middling 110.8833 1 1 Thayer, Mrs. John Borland (Marian Longstreth Morris) female 39.0000 1 1 17421 C68 C
cheap 79.2000 1 1 Thorne, Mrs. Gertrude Maybelle female NA 0 0 PC 17585 NA C
cheap 28.5375 1 1 Tucker, Mr. Gilbert Milligan Jr male 31.0000 0 0 2543 C53 C
cheap 27.7208 1 0 Uruchurtu, Don. Manuel E male 40.0000 0 0 PC 17601 NA C
cheap 33.5000 1 0 Van der hoef, Mr. Wyckoff male 61.0000 0 0 111240 B19 S
cheap 34.0208 1 0 Walker, Mr. William Anderson male 47.0000 0 0 36967 D46 S
very expensive 512.3292 1 1 Ward, Miss. Anna female 35.0000 0 0 PC 17755 NA C
cheap 75.2500 1 0 Warren, Mr. Frank Manley male 64.0000 1 0 110813 D37 C
cheap 75.2500 1 1 Warren, Mrs. Frank Manley (Anna Sophia Atkinson) female 60.0000 1 0 110813 D37 C
cheap 26.5500 1 0 Weir, Col. John male 60.0000 0 0 113800 NA S
cheap 77.2875 1 0 White, Mr. Percival Wayland male 54.0000 0 1 35281 D26 S
cheap 77.2875 1 0 White, Mr. Richard Frasar male 21.0000 0 1 35281 D26 S
middling 135.6333 1 1 White, Mrs. John Stuart (Ella Holmes) female 55.0000 0 0 PC 17760 C32 C
middling 164.8667 1 1 Wick, Miss. Mary Natalie female 31.0000 0 2 36928 C7 S
middling 164.8667 1 0 Wick, Mr. George Dennick male 57.0000 1 1 36928 NA S
middling 164.8667 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.0000 1 1 36928 NA S
expensive 211.5000 1 0 Widener, Mr. George Dunton male 50.0000 1 1 113503 C80 C
expensive 211.5000 1 0 Widener, Mr. Harry Elkins male 27.0000 0 2 113503 C82 C
expensive 211.5000 1 1 Widener, Mrs. George Dunton (Eleanor Elkins) female 50.0000 1 1 113503 C80 C
cheap 26.5500 1 1 Willard, Miss. Constance female 21.0000 0 0 113795 NA S
cheap 61.3792 1 0 Williams, Mr. Charles Duane male 51.0000 0 1 PC 17597 NA C
cheap 61.3792 1 1 Williams, Mr. Richard Norris II male 21.0000 0 1 PC 17597 NA C
cheap 35.0000 1 0 Williams-Lambert, Mr. Fletcher Fellows male NA 0 0 113510 C128 S
middling 134.5000 1 1 Wilson, Miss. Helen Alice female 31.0000 0 0 16966 E39 E41 C
cheap 35.5000 1 1 Woolner, Mr. Hugh male NA 0 0 19947 C52 S
cheap 26.5500 1 0 Wright, Mr. George male 62.0000 0 0 113807 NA S
middling 135.6333 1 1 Young, Miss. Marie Grice female 36.0000 0 0 PC 17760 C32 C
cheap 24.0000 2 0 Abelson, Mr. Samuel male 30.0000 1 0 P/PP 3381 NA C
cheap 24.0000 2 1 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0000 1 0 P/PP 3381 NA C
cheap 13.0000 2 0 Aldworth, Mr. Charles Augustus male 30.0000 0 0 248744 NA S
cheap 11.5000 2 0 Andrew, Mr. Edgardo Samuel male 18.0000 0 0 231945 NA S
cheap 10.5000 2 0 Andrew, Mr. Frank Thomas male 25.0000 0 0 C.A. 34050 NA S
cheap 26.0000 2 0 Angle, Mr. William A male 34.0000 1 0 226875 NA S
cheap 26.0000 2 1 Angle, Mrs. William A (Florence "Mary" Agnes Hughes) female 36.0000 1 0 226875 NA S
cheap 13.0000 2 0 Ashby, Mr. John male 57.0000 0 0 244346 NA S
cheap 11.5000 2 0 Bailey, Mr. Percy Andrew male 18.0000 0 0 29108 NA S
cheap 10.5000 2 0 Baimbrigge, Mr. Charles Robert male 23.0000 0 0 C.A. 31030 NA S
cheap 13.0000 2 1 Ball, Mrs. (Ada E Hall) female 36.0000 0 0 28551 D S
cheap 10.5000 2 0 Banfield, Mr. Frederick James male 28.0000 0 0 C.A./SOTON 34068 NA S
cheap 12.5250 2 0 Bateman, Rev. Robert James male 51.0000 0 0 S.O.P. 1166 NA S
cheap 26.0000 2 1 Beane, Mr. Edward male 32.0000 1 0 2908 NA S
cheap 26.0000 2 1 Beane, Mrs. Edward (Ethel Clarke) female 19.0000 1 0 2908 NA S
cheap 26.0000 2 0 Beauchamp, Mr. Henry James male 28.0000 0 0 244358 NA S
cheap 39.0000 2 1 Becker, Master. Richard F male 1.0000 2 1 230136 F4 S
cheap 39.0000 2 1 Becker, Miss. Marion Louise female 4.0000 2 1 230136 F4 S
cheap 39.0000 2 1 Becker, Miss. Ruth Elizabeth female 12.0000 2 1 230136 F4 S
cheap 39.0000 2 1 Becker, Mrs. Allen Oliver (Nellie E Baumgardner) female 36.0000 0 3 230136 F4 S
cheap 13.0000 2 1 Beesley, Mr. Lawrence male 34.0000 0 0 248698 D56 S
cheap 13.0000 2 1 Bentham, Miss. Lilian W female 19.0000 0 0 28404 NA S
cheap 13.0000 2 0 Berriman, Mr. William John male 23.0000 0 0 28425 NA S
cheap 13.0000 2 0 Botsford, Mr. William Hull male 26.0000 0 0 237670 NA S
cheap 13.0000 2 0 Bowenur, Mr. Solomon male 42.0000 0 0 211535 NA S
cheap 13.0000 2 0 Bracken, Mr. James H male 27.0000 0 0 220367 NA S
cheap 13.0000 2 1 Brown, Miss. Amelia "Mildred" female 24.0000 0 0 248733 F33 S
cheap 39.0000 2 1 Brown, Miss. Edith Eileen female 15.0000 0 2 29750 NA S
cheap 39.0000 2 0 Brown, Mr. Thomas William Solomon male 60.0000 1 1 29750 NA S
cheap 39.0000 2 1 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40.0000 1 1 29750 NA S
cheap 26.0000 2 1 Bryhl, Miss. Dagmar Jenny Ingeborg female 20.0000 1 0 236853 NA S
cheap 26.0000 2 0 Bryhl, Mr. Kurt Arnold Gottfrid male 25.0000 1 0 236853 NA S
cheap 13.0000 2 1 Buss, Miss. Kate female 36.0000 0 0 27849 NA S
cheap 13.0000 2 0 Butler, Mr. Reginald Fenton male 25.0000 0 0 234686 NA S
cheap 13.0000 2 0 Byles, Rev. Thomas Roussel Davids male 42.0000 0 0 244310 NA S
cheap 13.0000 2 1 Bystrom, Mrs. (Karolina) female 42.0000 0 0 236852 NA S
cheap 29.0000 2 1 Caldwell, Master. Alden Gates male 0.8333 0 2 248738 NA S
cheap 29.0000 2 1 Caldwell, Mr. Albert Francis male 26.0000 1 1 248738 NA S
cheap 29.0000 2 1 Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) female 22.0000 1 1 248738 NA S
cheap 21.0000 2 1 Cameron, Miss. Clear Annie female 35.0000 0 0 F.C.C. 13528 NA S
cheap 0.0000 2 0 Campbell, Mr. William male NA 0 0 239853 NA S
cheap 13.0000 2 0 Carbines, Mr. William male 19.0000 0 0 28424 NA S
cheap 26.0000 2 0 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44.0000 1 0 244252 NA S
cheap 26.0000 2 0 Carter, Rev. Ernest Courtenay male 54.0000 1 0 244252 NA S
cheap 13.5000 2 0 Chapman, Mr. Charles Henry male 52.0000 0 0 248731 NA S
cheap 26.0000 2 0 Chapman, Mr. John Henry male 37.0000 1 0 SC/AH 29037 NA S
cheap 26.0000 2 0 Chapman, Mrs. John Henry (Sara Elizabeth Lawry) female 29.0000 1 0 SC/AH 29037 NA S
cheap 30.0000 2 1 Christy, Miss. Julie Rachel female 25.0000 1 1 237789 NA S
cheap 30.0000 2 1 Christy, Mrs. (Alice Frances) female 45.0000 0 2 237789 NA S
cheap 26.0000 2 0 Clarke, Mr. Charles Valentine male 29.0000 1 0 2003 NA S
cheap 26.0000 2 1 Clarke, Mrs. Charles V (Ada Maria Winfield) female 28.0000 1 0 2003 NA S
cheap 10.5000 2 0 Coleridge, Mr. Reginald Charles male 29.0000 0 0 W./C. 14263 NA S
cheap 13.0000 2 0 Collander, Mr. Erik Gustaf male 28.0000 0 0 248740 NA S
cheap 10.5000 2 1 Collett, Mr. Sidney C Stuart male 24.0000 0 0 28034 NA S
cheap 26.2500 2 1 Collyer, Miss. Marjorie "Lottie" female 8.0000 0 2 C.A. 31921 NA S
cheap 26.2500 2 0 Collyer, Mr. Harvey male 31.0000 1 1 C.A. 31921 NA S
cheap 26.2500 2 1 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31.0000 1 1 C.A. 31921 NA S
cheap 10.5000 2 1 Cook, Mrs. (Selena Rogers) female 22.0000 0 0 W./C. 14266 F33 S
cheap 13.0000 2 0 Corbett, Mrs. Walter H (Irene Colvin) female 30.0000 0 0 237249 NA S
cheap 21.0000 2 0 Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller) female NA 0 0 F.C.C. 13534 NA S
cheap 11.5000 2 0 Cotterill, Mr. Henry "Harry" male 21.0000 0 0 29107 NA S
cheap 0.0000 2 0 Cunningham, Mr. Alfred Fleming male NA 0 0 239853 NA S
cheap 36.7500 2 1 Davies, Master. John Morgan Jr male 8.0000 1 1 C.A. 33112 NA S
cheap 73.5000 2 0 Davies, Mr. Charles Henry male 18.0000 0 0 S.O.C. 14879 NA S
cheap 36.7500 2 1 Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) female 48.0000 0 2 C.A. 33112 NA S
cheap 13.0000 2 1 Davis, Miss. Mary female 28.0000 0 0 237668 NA S
cheap 13.0000 2 0 de Brito, Mr. Jose Joaquim male 32.0000 0 0 244360 NA S
cheap 73.5000 2 0 Deacon, Mr. Percy William male 17.0000 0 0 S.O.C. 14879 NA S
cheap 27.7208 2 0 del Carlo, Mr. Sebastiano male 29.0000 1 0 SC/PARIS 2167 NA C
cheap 27.7208 2 1 del Carlo, Mrs. Sebastiano (Argenia Genovesi) female 24.0000 1 0 SC/PARIS 2167 NA C
cheap 31.5000 2 0 Denbury, Mr. Herbert male 25.0000 0 0 C.A. 31029 NA S
cheap 73.5000 2 0 Dibden, Mr. William male 18.0000 0 0 S.O.C. 14879 NA S
cheap 23.0000 2 1 Doling, Miss. Elsie female 18.0000 0 1 231919 NA S
cheap 23.0000 2 1 Doling, Mrs. John T (Ada Julia Bone) female 34.0000 0 1 231919 NA S
cheap 26.0000 2 0 Downton, Mr. William James male 54.0000 0 0 28403 NA S
cheap 32.5000 2 1 Drew, Master. Marshall Brines male 8.0000 0 2 28220 NA S
cheap 32.5000 2 0 Drew, Mr. James Vivian male 42.0000 1 1 28220 NA S
cheap 32.5000 2 1 Drew, Mrs. James Vivian (Lulu Thorne Christian) female 34.0000 1 1 28220 NA S
cheap 13.8583 2 1 Duran y More, Miss. Asuncion female 27.0000 1 0 SC/PARIS 2149 NA C
cheap 13.8583 2 1 Duran y More, Miss. Florentina female 30.0000 1 0 SC/PARIS 2148 NA C
cheap 13.0000 2 0 Eitemiller, Mr. George Floyd male 23.0000 0 0 29751 NA S
cheap 13.0000 2 0 Enander, Mr. Ingvar male 21.0000 0 0 236854 NA S
cheap 13.0000 2 0 Fahlstrom, Mr. Arne Jonas male 18.0000 0 0 236171 NA S
cheap 26.0000 2 0 Faunthorpe, Mr. Harry male 40.0000 1 0 2926 NA S
cheap 26.0000 2 1 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29.0000 1 0 2926 NA S
cheap 10.5000 2 0 Fillbrook, Mr. Joseph Charles male 18.0000 0 0 C.A. 15185 NA S
cheap 13.0000 2 0 Fox, Mr. Stanley Hubert male 36.0000 0 0 229236 NA S
cheap 0.0000 2 0 Frost, Mr. Anthony Wood "Archie" male NA 0 0 239854 NA S
cheap 13.0000 2 0 Funk, Miss. Annie Clemmer female 38.0000 0 0 237671 NA S
cheap 26.0000 2 0 Fynney, Mr. Joseph J male 35.0000 0 0 239865 NA S
cheap 21.0000 2 0 Gale, Mr. Harry male 38.0000 1 0 28664 NA S
cheap 21.0000 2 0 Gale, Mr. Shadrach male 34.0000 1 0 28664 NA S
cheap 13.0000 2 1 Garside, Miss. Ethel female 34.0000 0 0 243880 NA S
cheap 26.0000 2 0 Gaskell, Mr. Alfred male 16.0000 0 0 239865 NA S
cheap 10.5000 2 0 Gavey, Mr. Lawrence male 26.0000 0 0 31028 NA S
cheap 10.5000 2 0 Gilbert, Mr. William male 47.0000 0 0 C.A. 30769 NA S
cheap 11.5000 2 0 Giles, Mr. Edgar male 21.0000 1 0 28133 NA S
cheap 11.5000 2 0 Giles, Mr. Frederick Edward male 21.0000 1 0 28134 NA S
cheap 13.5000 2 0 Giles, Mr. Ralph male 24.0000 0 0 248726 NA S
cheap 13.0000 2 0 Gill, Mr. John William male 24.0000 0 0 233866 NA S
cheap 13.0000 2 0 Gillespie, Mr. William Henry male 34.0000 0 0 12233 NA S
cheap 13.0000 2 0 Givard, Mr. Hans Kristensen male 30.0000 0 0 250646 NA S
cheap 13.0000 2 0 Greenberg, Mr. Samuel male 52.0000 0 0 250647 NA S
cheap 13.0000 2 0 Hale, Mr. Reginald male 30.0000 0 0 250653 NA S
cheap 14.5000 2 1 Hamalainen, Master. Viljo male 0.6667 1 1 250649 NA S
cheap 14.5000 2 1 Hamalainen, Mrs. William (Anna) female 24.0000 0 2 250649 NA S
cheap 13.0000 2 0 Harbeck, Mr. William H male 44.0000 0 0 248746 NA S
cheap 33.0000 2 1 Harper, Miss. Annie Jessie "Nina" female 6.0000 0 1 248727 NA S
cheap 33.0000 2 0 Harper, Rev. John male 28.0000 0 1 248727 NA S
cheap 10.5000 2 1 Harris, Mr. George male 62.0000 0 0 S.W./PP 752 NA S
cheap 10.5000 2 0 Harris, Mr. Walter male 30.0000 0 0 W/C 14208 NA S
cheap 26.2500 2 1 Hart, Miss. Eva Miriam female 7.0000 0 2 F.C.C. 13529 NA S
cheap 26.2500 2 0 Hart, Mr. Benjamin male 43.0000 1 1 F.C.C. 13529 NA S
cheap 26.2500 2 1 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45.0000 1 1 F.C.C. 13529 NA S
cheap 65.0000 2 1 Herman, Miss. Alice female 24.0000 1 2 220845 NA S
cheap 65.0000 2 1 Herman, Miss. Kate female 24.0000 1 2 220845 NA S
cheap 65.0000 2 0 Herman, Mr. Samuel male 49.0000 1 2 220845 NA S
cheap 65.0000 2 1 Herman, Mrs. Samuel (Jane Laver) female 48.0000 1 2 220845 NA S
cheap 16.0000 2 1 Hewlett, Mrs. (Mary D Kingcome) female 55.0000 0 0 248706 NA S
cheap 73.5000 2 0 Hickman, Mr. Leonard Mark male 24.0000 2 0 S.O.C. 14879 NA S
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cheap 9.0000 3 0 Vander Cruyssen, Mr. Victor male 47.0000 0 0 345765 NA S
cheap 18.0000 3 0 Vander Planke, Miss. Augusta Maria female 18.0000 2 0 345764 NA S
cheap 18.0000 3 0 Vander Planke, Mr. Julius male 31.0000 3 0 345763 NA S
cheap 18.0000 3 0 Vander Planke, Mr. Leo Edmondus male 16.0000 2 0 345764 NA S
cheap 18.0000 3 0 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.0000 1 0 345763 NA S
cheap 7.2250 3 1 Vartanian, Mr. David male 22.0000 0 0 2658 NA C
cheap 7.8542 3 0 Vendel, Mr. Olof Edvin male 20.0000 0 0 350416 NA S
cheap 7.8542 3 0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0000 0 0 350406 NA S
cheap 7.8958 3 0 Vovk, Mr. Janko male 22.0000 0 0 349252 NA S
cheap 9.0000 3 0 Waelens, Mr. Achille male 22.0000 0 0 345767 NA S
cheap 8.0500 3 0 Ware, Mr. Frederick male NA 0 0 359309 NA S
cheap 7.5500 3 0 Warren, Mr. Charles William male NA 0 0 C.A. 49867 NA S
cheap 8.0500 3 0 Webber, Mr. James male NA 0 0 SOTON/OQ 3101316 NA S
cheap 9.5000 3 0 Wenzel, Mr. Linhart male 32.5000 0 0 345775 NA S
cheap 7.2292 3 1 Whabee, Mrs. George Joseph (Shawneene Abi-Saab) female 38.0000 0 0 2688 NA C
cheap 7.7500 3 0 Widegren, Mr. Carl/Charles Peter male 51.0000 0 0 347064 NA S
cheap 6.4958 3 0 Wiklund, Mr. Jakob Alfred male 18.0000 1 0 3101267 NA S
cheap 6.4958 3 0 Wiklund, Mr. Karl Johan male 21.0000 1 0 3101266 NA S
cheap 7.0000 3 1 Wilkes, Mrs. James (Ellen Needs) female 47.0000 1 0 363272 NA S
cheap 8.7125 3 0 Willer, Mr. Aaron ("Abi Weller") male NA 0 0 3410 NA S
cheap 7.5500 3 0 Willey, Mr. Edward male NA 0 0 S.O./P.P. 751 NA S
cheap 8.0500 3 0 Williams, Mr. Howard Hugh "Harry" male NA 0 0 A/5 2466 NA S
cheap 16.1000 3 0 Williams, Mr. Leslie male 28.5000 0 0 54636 NA S
cheap 7.2500 3 0 Windelov, Mr. Einar male 21.0000 0 0 SOTON/OQ 3101317 NA S
cheap 8.6625 3 0 Wirz, Mr. Albert male 27.0000 0 0 315154 NA S
cheap 7.2500 3 0 Wiseman, Mr. Phillippe male NA 0 0 A/4. 34244 NA S
cheap 9.5000 3 0 Wittevrongel, Mr. Camille male 36.0000 0 0 345771 NA S
cheap 14.4542 3 0 Yasbeck, Mr. Antoni male 27.0000 1 0 2659 NA C
cheap 14.4542 3 1 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0000 1 0 2659 NA C
cheap 7.2250 3 0 Youseff, Mr. Gerious male 45.5000 0 0 2628 NA C
cheap 7.2250 3 0 Yousif, Mr. Wazli male NA 0 0 2647 NA C
cheap 14.4583 3 0 Yousseff, Mr. Gerious male NA 0 0 2627 NA C
cheap 14.4542 3 0 Zabour, Miss. Hileni female 14.5000 1 0 2665 NA C
cheap 14.4542 3 0 Zabour, Miss. Thamine female NA 1 0 2665 NA C
cheap 7.2250 3 0 Zakarian, Mr. Mapriededer male 26.5000 0 0 2656 NA C
cheap 7.2250 3 0 Zakarian, Mr. Ortin male 27.0000 0 0 2670 NA C
cheap 7.8750 3 0 Zimmerman, Mr. Leo male 29.0000 0 0 315082 NA S

I can do a count of my data, we can see below that we have one unknown level.

Can you take a guess why?

# Geting a count of the levels created
# Notice we have 1 unknown value
# This is because we have 1 missing value in the fare column
# Which we used to create the levels

titanic_fare_level %>% 
        dplyr::count(fare_level, sort = TRUE)
# A tibble: 5 × 2
  fare_level         n
  <chr>          <int>
1 cheap           1224
2 middling          46
3 expensive         34
4 very expensive     4
5 unknown            1

4.4.2 Extension Exercise Case When

Building on from the earlier exercise when we imputed missing values using the mean value of the of the age_of_passenger column.

Use the case_when function to create a new column in the titanic dataset called imputed_age_of_passenger.

In this column we should have,

  • wherever the value of the sex_of_passenger is “male” and age of passenger value is missing the imputed value should be the mean age_of_passenger of only the male passengers.

  • wherever the value sex_of_passenger is “female” and age of passenger value is missing the imputed value should be imputed with the mean age_of_passenger of only the female passengers.

  • otherwise, take the value of the age_of_passenger.

As a guide the mean value for female passenger is 28.6 and for male passengers is 30.5.

As a guide the mean value for female passenger is 28.6 and for male passengers is 30.5.

# Please NOTE, this is not code, shows the steps required 

# Calculate the mean values for male and female passengers

# Use case_when within a mutate to add the new column

titanic_imputed_mean_values <- titanic %>% 
  dplyr::mutate(imputed_age_of_passenger = dplyr::case_when(
            condtion ~ value,
            condition ~ value
            TRUE ~ value)
  )

Building on from the earlier exercise when we imputed missing values using the mean value of the of the age_of_passenger column.

Use the case_when function to create a new column in the titanic dataset called imputed_age_of_passenger.

In this column we should have,

  • wherever the value of the sex_of_passenger is “male” and age of passenger value is missing the imputed value should be the mean age_of_passenger of only the male passengers.

  • wherever the value sex_of_passenger is “female” and age of passenger value is missing the imputed value should be imputed with the mean age_of_passenger of only the female passengers.

  • otherwise, take the value of the age_of_passenger.

As a guide the mean value for female passenger is 28.6 and for male passengers is 30.5.

# Calculating the mean value for male passengers
# Here I am assigning the value to male_passenger_mean_age
# Firstly filtering the titanic data for male passengers
# Then using the pull function to pull out a a single variable/vector
# Then calculating the mean of the age excluding missing values

male_passenger_mean_age <- titanic %>% 
  dplyr::filter(sex_of_passenger == "male") %>%
  dplyr::pull(age_of_passenger) %>% 
  mean(na.rm = TRUE)

# Calculating the mean value for female passengers
# Here I am assigning the value to female_passenger_mean_age
# firstly filtering the titanic data for female passengers 
# Then calculating the mean of the age excluding missing values

female_passenger_mean_age <- titanic %>% 
  dplyr::filter(sex_of_passenger == "female") %>%
  dplyr::pull(age_of_passenger) %>% 
  mean(na.rm = TRUE)

# Using case_when
# Here I am assigning it to titanic_imputed_mean_values
# using mutate to assign the new column
# case_when() specified inside the mutate function

titanic_imputed_mean_values <- titanic %>% 
  dplyr::mutate(imputed_age_of_passenger = dplyr::case_when(
            sex_of_passenger == "male" & is.na(age_of_passenger) ~ male_passenger_mean_age,
            sex_of_passenger == "female" & is.na(age_of_passenger) ~ female_passenger_mean_age,
            TRUE ~ age_of_passenger)
  )
sex_of_passenger age_of_passenger imputed_age_of_passenger pclass survived name_of_passenger sibsp parch ticket fare cabin embarked
female 29.0000 29.00000 1 1 Allen, Miss. Elisabeth Walton 0 0 24160 211.3375 B5 S
male 0.9167 0.91670 1 1 Allison, Master. Hudson Trevor 1 2 113781 151.5500 C22 C26 S
female 2.0000 2.00000 1 0 Allison, Miss. Helen Loraine 1 2 113781 151.5500 C22 C26 S
male 30.0000 30.00000 1 0 Allison, Mr. Hudson Joshua Creighton 1 2 113781 151.5500 C22 C26 S
female 25.0000 25.00000 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 1 2 113781 151.5500 C22 C26 S
male 48.0000 48.00000 1 1 Anderson, Mr. Harry 0 0 19952 26.5500 E12 S
female 63.0000 63.00000 1 1 Andrews, Miss. Kornelia Theodosia 1 0 13502 77.9583 D7 S
male NA 30.48562 1 0 Andrews, Mr. Thomas Jr 0 0 112050 0.0000 A36 S
female NA 28.62425 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) 2 0 11769 51.4792 C101 S
male NA 30.48562 1 0 Artagaveytia, Mr. Ramon 0 0 PC 17609 49.5042 NA C
male NA 30.48562 1 0 Astor, Col. John Jacob 1 0 PC 17757 227.5250 C62 C64 C
female 18.0000 18.00000 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) 1 0 PC 17757 227.5250 C62 C64 C
female 24.0000 24.00000 1 1 Aubart, Mme. Leontine Pauline 0 0 PC 17477 69.3000 B35 C
female 26.0000 26.00000 1 1 Barber, Miss. Ellen "Nellie" 0 0 19877 78.8500 NA S
male 80.0000 80.00000 1 1 Barkworth, Mr. Algernon Henry Wilson 0 0 27042 30.0000 A23 S
male NA 30.48562 1 0 Baumann, Mr. John D 0 0 PC 17318 25.9250 NA S
male 24.0000 24.00000 1 0 Baxter, Mr. Quigg Edmond 0 1 PC 17558 247.5208 B58 B60 C
female 50.0000 50.00000 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) 0 1 PC 17558 247.5208 B58 B60 C
female 32.0000 32.00000 1 1 Bazzani, Miss. Albina 0 0 11813 76.2917 D15 C
male 36.0000 36.00000 1 0 Beattie, Mr. Thomson 0 0 13050 75.2417 C6 C
male 37.0000 37.00000 1 1 Beckwith, Mr. Richard Leonard 1 1 11751 52.5542 D35 S
female 47.0000 47.00000 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 1 1 11751 52.5542 D35 S
male 26.0000 26.00000 1 1 Behr, Mr. Karl Howell 0 0 111369 30.0000 C148 C
female 42.0000 42.00000 1 1 Bidois, Miss. Rosalie 0 0 PC 17757 227.5250 NA C
female 29.0000 29.00000 1 1 Bird, Miss. Ellen 0 0 PC 17483 221.7792 C97 S
male 25.0000 25.00000 1 0 Birnbaum, Mr. Jakob 0 0 13905 26.0000 NA C
male 25.0000 25.00000 1 1 Bishop, Mr. Dickinson H 1 0 11967 91.0792 B49 C
female 19.0000 19.00000 1 1 Bishop, Mrs. Dickinson H (Helen Walton) 1 0 11967 91.0792 B49 C
female 35.0000 35.00000 1 1 Bissette, Miss. Amelia 0 0 PC 17760 135.6333 C99 S
male 28.0000 28.00000 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan 0 0 110564 26.5500 C52 S
male 45.0000 45.00000 1 0 Blackwell, Mr. Stephen Weart 0 0 113784 35.5000 T S
male 40.0000 40.00000 1 1 Blank, Mr. Henry 0 0 112277 31.0000 A31 C
female 30.0000 30.00000 1 1 Bonnell, Miss. Caroline 0 0 36928 164.8667 C7 S
female 58.0000 58.00000 1 1 Bonnell, Miss. Elizabeth 0 0 113783 26.5500 C103 S
male 42.0000 42.00000 1 0 Borebank, Mr. John James 0 0 110489 26.5500 D22 S
female 45.0000 45.00000 1 1 Bowen, Miss. Grace Scott 0 0 PC 17608 262.3750 NA C
female 22.0000 22.00000 1 1 Bowerman, Miss. Elsie Edith 0 1 113505 55.0000 E33 S
male NA 30.48562 1 1 Bradley, Mr. George ("George Arthur Brayton") 0 0 111427 26.5500 NA S
male 41.0000 41.00000 1 0 Brady, Mr. John Bertram 0 0 113054 30.5000 A21 S
male 48.0000 48.00000 1 0 Brandeis, Mr. Emil 0 0 PC 17591 50.4958 B10 C
male NA 30.48562 1 0 Brewe, Dr. Arthur Jackson 0 0 112379 39.6000 NA C
female 44.0000 44.00000 1 1 Brown, Mrs. James Joseph (Margaret Tobin) 0 0 PC 17610 27.7208 B4 C
female 59.0000 59.00000 1 1 Brown, Mrs. John Murray (Caroline Lane Lamson) 2 0 11769 51.4792 C101 S
female 60.0000 60.00000 1 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) 0 0 11813 76.2917 D15 C
female 41.0000 41.00000 1 1 Burns, Miss. Elizabeth Margaret 0 0 16966 134.5000 E40 C
male 45.0000 45.00000 1 0 Butt, Major. Archibald Willingham 0 0 113050 26.5500 B38 S
male NA 30.48562 1 0 Cairns, Mr. Alexander 0 0 113798 31.0000 NA S
male 42.0000 42.00000 1 1 Calderhead, Mr. Edward Pennington 0 0 PC 17476 26.2875 E24 S
female 53.0000 53.00000 1 1 Candee, Mrs. Edward (Helen Churchill Hungerford) 0 0 PC 17606 27.4458 NA C
male 36.0000 36.00000 1 1 Cardeza, Mr. Thomas Drake Martinez 0 1 PC 17755 512.3292 B51 B53 B55 C
female 58.0000 58.00000 1 1 Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake) 0 1 PC 17755 512.3292 B51 B53 B55 C
male 33.0000 33.00000 1 0 Carlsson, Mr. Frans Olof 0 0 695 5.0000 B51 B53 B55 S
male 28.0000 28.00000 1 0 Carrau, Mr. Francisco M 0 0 113059 47.1000 NA S
male 17.0000 17.00000 1 0 Carrau, Mr. Jose Pedro 0 0 113059 47.1000 NA S
male 11.0000 11.00000 1 1 Carter, Master. William Thornton II 1 2 113760 120.0000 B96 B98 S
female 14.0000 14.00000 1 1 Carter, Miss. Lucile Polk 1 2 113760 120.0000 B96 B98 S
male 36.0000 36.00000 1 1 Carter, Mr. William Ernest 1 2 113760 120.0000 B96 B98 S
female 36.0000 36.00000 1 1 Carter, Mrs. William Ernest (Lucile Polk) 1 2 113760 120.0000 B96 B98 S
male 49.0000 49.00000 1 0 Case, Mr. Howard Brown 0 0 19924 26.0000 NA S
female NA 28.62425 1 1 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) 0 0 17770 27.7208 NA C
male 36.0000 36.00000 1 0 Cavendish, Mr. Tyrell William 1 0 19877 78.8500 C46 S
female 76.0000 76.00000 1 1 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) 1 0 19877 78.8500 C46 S
male 46.0000 46.00000 1 0 Chaffee, Mr. Herbert Fuller 1 0 W.E.P. 5734 61.1750 E31 S
female 47.0000 47.00000 1 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood) 1 0 W.E.P. 5734 61.1750 E31 S
male 27.0000 27.00000 1 1 Chambers, Mr. Norman Campbell 1 0 113806 53.1000 E8 S
female 33.0000 33.00000 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) 1 0 113806 53.1000 E8 S
female 36.0000 36.00000 1 1 Chaudanson, Miss. Victorine 0 0 PC 17608 262.3750 B61 C
female 30.0000 30.00000 1 1 Cherry, Miss. Gladys 0 0 110152 86.5000 B77 S
male 45.0000 45.00000 1 1 Chevre, Mr. Paul Romaine 0 0 PC 17594 29.7000 A9 C
female NA 28.62425 1 1 Chibnall, Mrs. (Edith Martha Bowerman) 0 1 113505 55.0000 E33 S
male NA 30.48562 1 0 Chisholm, Mr. Roderick Robert Crispin 0 0 112051 0.0000 NA S
male 27.0000 27.00000 1 0 Clark, Mr. Walter Miller 1 0 13508 136.7792 C89 C
female 26.0000 26.00000 1 1 Clark, Mrs. Walter Miller (Virginia McDowell) 1 0 13508 136.7792 C89 C
female 22.0000 22.00000 1 1 Cleaver, Miss. Alice 0 0 113781 151.5500 NA S
male NA 30.48562 1 0 Clifford, Mr. George Quincy 0 0 110465 52.0000 A14 S
male 47.0000 47.00000 1 0 Colley, Mr. Edward Pomeroy 0 0 5727 25.5875 E58 S
female 39.0000 39.00000 1 1 Compton, Miss. Sara Rebecca 1 1 PC 17756 83.1583 E49 C
male 37.0000 37.00000 1 0 Compton, Mr. Alexander Taylor Jr 1 1 PC 17756 83.1583 E52 C
female 64.0000 64.00000 1 1 Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) 0 2 PC 17756 83.1583 E45 C
female 55.0000 55.00000 1 1 Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) 2 0 11770 25.7000 C101 S
male NA 30.48562 1 0 Crafton, Mr. John Bertram 0 0 113791 26.5500 NA S
male 70.0000 70.00000 1 0 Crosby, Capt. Edward Gifford 1 1 WE/P 5735 71.0000 B22 S
female 36.0000 36.00000 1 1 Crosby, Miss. Harriet R 0 2 WE/P 5735 71.0000 B22 S
female 64.0000 64.00000 1 1 Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) 1 1 112901 26.5500 B26 S
male 39.0000 39.00000 1 0 Cumings, Mr. John Bradley 1 0 PC 17599 71.2833 C85 C
female 38.0000 38.00000 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) 1 0 PC 17599 71.2833 C85 C
male 51.0000 51.00000 1 1 Daly, Mr. Peter Denis 0 0 113055 26.5500 E17 S
male 27.0000 27.00000 1 1 Daniel, Mr. Robert Williams 0 0 113804 30.5000 NA S
female 33.0000 33.00000 1 1 Daniels, Miss. Sarah 0 0 113781 151.5500 NA S
male 31.0000 31.00000 1 0 Davidson, Mr. Thornton 1 0 F.C. 12750 52.0000 B71 S
female 27.0000 27.00000 1 1 Davidson, Mrs. Thornton (Orian Hays) 1 2 F.C. 12750 52.0000 B71 S
male 31.0000 31.00000 1 1 Dick, Mr. Albert Adrian 1 0 17474 57.0000 B20 S
female 17.0000 17.00000 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) 1 0 17474 57.0000 B20 S
male 53.0000 53.00000 1 1 Dodge, Dr. Washington 1 1 33638 81.8583 A34 S
male 4.0000 4.00000 1 1 Dodge, Master. Washington 0 2 33638 81.8583 A34 S
female 54.0000 54.00000 1 1 Dodge, Mrs. Washington (Ruth Vidaver) 1 1 33638 81.8583 A34 S
male 50.0000 50.00000 1 0 Douglas, Mr. Walter Donald 1 0 PC 17761 106.4250 C86 C
female 27.0000 27.00000 1 1 Douglas, Mrs. Frederick Charles (Mary Helene Baxter) 1 1 PC 17558 247.5208 B58 B60 C
female 48.0000 48.00000 1 1 Douglas, Mrs. Walter Donald (Mahala Dutton) 1 0 PC 17761 106.4250 C86 C
female 48.0000 48.00000 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") 1 0 11755 39.6000 A16 C
male 49.0000 49.00000 1 1 Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") 1 0 PC 17485 56.9292 A20 C
male 39.0000 39.00000 1 0 Dulles, Mr. William Crothers 0 0 PC 17580 29.7000 A18 C
female 23.0000 23.00000 1 1 Earnshaw, Mrs. Boulton (Olive Potter) 0 1 11767 83.1583 C54 C
female 38.0000 38.00000 1 1 Endres, Miss. Caroline Louise 0 0 PC 17757 227.5250 C45 C
female 54.0000 54.00000 1 1 Eustis, Miss. Elizabeth Mussey 1 0 36947 78.2667 D20 C
female 36.0000 36.00000 1 0 Evans, Miss. Edith Corse 0 0 PC 17531 31.6792 A29 C
male NA 30.48562 1 0 Farthing, Mr. John 0 0 PC 17483 221.7792 C95 S
female NA 28.62425 1 1 Flegenheim, Mrs. Alfred (Antoinette) 0 0 PC 17598 31.6833 NA S
female NA 28.62425 1 1 Fleming, Miss. Margaret 0 0 17421 110.8833 NA C
male 36.0000 36.00000 1 1 Flynn, Mr. John Irwin ("Irving") 0 0 PC 17474 26.3875 E25 S
male 30.0000 30.00000 1 0 Foreman, Mr. Benjamin Laventall 0 0 113051 27.7500 C111 C
female 24.0000 24.00000 1 1 Fortune, Miss. Alice Elizabeth 3 2 19950 263.0000 C23 C25 C27 S
female 28.0000 28.00000 1 1 Fortune, Miss. Ethel Flora 3 2 19950 263.0000 C23 C25 C27 S
female 23.0000 23.00000 1 1 Fortune, Miss. Mabel Helen 3 2 19950 263.0000 C23 C25 C27 S
male 19.0000 19.00000 1 0 Fortune, Mr. Charles Alexander 3 2 19950 263.0000 C23 C25 C27 S
male 64.0000 64.00000 1 0 Fortune, Mr. Mark 1 4 19950 263.0000 C23 C25 C27 S
female 60.0000 60.00000 1 1 Fortune, Mrs. Mark (Mary McDougald) 1 4 19950 263.0000 C23 C25 C27 S
female 30.0000 30.00000 1 1 Francatelli, Miss. Laura Mabel 0 0 PC 17485 56.9292 E36 C
male NA 30.48562 1 0 Franklin, Mr. Thomas Parham 0 0 113778 26.5500 D34 S
male 50.0000 50.00000 1 1 Frauenthal, Dr. Henry William 2 0 PC 17611 133.6500 NA S
male 43.0000 43.00000 1 1 Frauenthal, Mr. Isaac Gerald 1 0 17765 27.7208 D40 C
female NA 28.62425 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) 1 0 PC 17611 133.6500 NA S
female 22.0000 22.00000 1 1 Frolicher, Miss. Hedwig Margaritha 0 2 13568 49.5000 B39 C
male 60.0000 60.00000 1 1 Frolicher-Stehli, Mr. Maxmillian 1 1 13567 79.2000 B41 C
female 48.0000 48.00000 1 1 Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli) 1 1 13567 79.2000 B41 C
male NA 30.48562 1 0 Fry, Mr. Richard 0 0 112058 0.0000 B102 S
male 37.0000 37.00000 1 0 Futrelle, Mr. Jacques Heath 1 0 113803 53.1000 C123 S
female 35.0000 35.00000 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 0 113803 53.1000 C123 S
male 47.0000 47.00000 1 0 Gee, Mr. Arthur H 0 0 111320 38.5000 E63 S
female 35.0000 35.00000 1 1 Geiger, Miss. Amalie 0 0 113503 211.5000 C130 C
female 22.0000 22.00000 1 1 Gibson, Miss. Dorothy Winifred 0 1 112378 59.4000 NA C
female 45.0000 45.00000 1 1 Gibson, Mrs. Leonard (Pauline C Boeson) 0 1 112378 59.4000 NA C
male 24.0000 24.00000 1 0 Giglio, Mr. Victor 0 0 PC 17593 79.2000 B86 C
male 49.0000 49.00000 1 1 Goldenberg, Mr. Samuel L 1 0 17453 89.1042 C92 C
female NA 28.62425 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) 1 0 17453 89.1042 C92 C
male 71.0000 71.00000 1 0 Goldschmidt, Mr. George B 0 0 PC 17754 34.6542 A5 C
male 53.0000 53.00000 1 1 Gracie, Col. Archibald IV 0 0 113780 28.5000 C51 C
female 19.0000 19.00000 1 1 Graham, Miss. Margaret Edith 0 0 112053 30.0000 B42 S
male 38.0000 38.00000 1 0 Graham, Mr. George Edward 0 1 PC 17582 153.4625 C91 S
female 58.0000 58.00000 1 1 Graham, Mrs. William Thompson (Edith Junkins) 0 1 PC 17582 153.4625 C125 S
male 23.0000 23.00000 1 1 Greenfield, Mr. William Bertram 0 1 PC 17759 63.3583 D10 D12 C
female 45.0000 45.00000 1 1 Greenfield, Mrs. Leo David (Blanche Strouse) 0 1 PC 17759 63.3583 D10 D12 C
male 46.0000 46.00000 1 0 Guggenheim, Mr. Benjamin 0 0 PC 17593 79.2000 B82 B84 C
male 25.0000 25.00000 1 1 Harder, Mr. George Achilles 1 0 11765 55.4417 E50 C
female 25.0000 25.00000 1 1 Harder, Mrs. George Achilles (Dorothy Annan) 1 0 11765 55.4417 E50 C
male 48.0000 48.00000 1 1 Harper, Mr. Henry Sleeper 1 0 PC 17572 76.7292 D33 C
female 49.0000 49.00000 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) 1 0 PC 17572 76.7292 D33 C
male NA 30.48562 1 0 Harrington, Mr. Charles H 0 0 113796 42.4000 NA S
male 45.0000 45.00000 1 0 Harris, Mr. Henry Birkhardt 1 0 36973 83.4750 C83 S
female 35.0000 35.00000 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) 1 0 36973 83.4750 C83 S
male 40.0000 40.00000 1 0 Harrison, Mr. William 0 0 112059 0.0000 B94 S
male 27.0000 27.00000 1 1 Hassab, Mr. Hammad 0 0 PC 17572 76.7292 D49 C
male NA 30.48562 1 1 Hawksford, Mr. Walter James 0 0 16988 30.0000 D45 S
female 24.0000 24.00000 1 1 Hays, Miss. Margaret Bechstein 0 0 11767 83.1583 C54 C
male 55.0000 55.00000 1 0 Hays, Mr. Charles Melville 1 1 12749 93.5000 B69 S
female 52.0000 52.00000 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) 1 1 12749 93.5000 B69 S
male 42.0000 42.00000 1 0 Head, Mr. Christopher 0 0 113038 42.5000 B11 S
male NA 30.48562 1 0 Hilliard, Mr. Herbert Henry 0 0 17463 51.8625 E46 S
male 55.0000 55.00000 1 0 Hipkins, Mr. William Edward 0 0 680 50.0000 C39 S
female 16.0000 16.00000 1 1 Hippach, Miss. Jean Gertrude 0 1 111361 57.9792 B18 C
female 44.0000 44.00000 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) 0 1 111361 57.9792 B18 C
female 51.0000 51.00000 1 1 Hogeboom, Mrs. John C (Anna Andrews) 1 0 13502 77.9583 D11 S
male 42.0000 42.00000 1 0 Holverson, Mr. Alexander Oskar 1 0 113789 52.0000 NA S
female 35.0000 35.00000 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) 1 0 113789 52.0000 NA S
male 35.0000 35.00000 1 1 Homer, Mr. Harry ("Mr E Haven") 0 0 111426 26.5500 NA C
male 38.0000 38.00000 1 1 Hoyt, Mr. Frederick Maxfield 1 0 19943 90.0000 C93 S
male NA 30.48562 1 0 Hoyt, Mr. William Fisher 0 0 PC 17600 30.6958 NA C
female 35.0000 35.00000 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) 1 0 19943 90.0000 C93 S
female 38.0000 38.00000 1 1 Icard, Miss. Amelie 0 0 113572 80.0000 B28 NA
female 50.0000 50.00000 1 0 Isham, Miss. Ann Elizabeth 0 0 PC 17595 28.7125 C49 C
male 49.0000 49.00000 1 1 Ismay, Mr. Joseph Bruce 0 0 112058 0.0000 B52 B54 B56 S
male 46.0000 46.00000 1 0 Jones, Mr. Charles Cresson 0 0 694 26.0000 NA S
male 50.0000 50.00000 1 0 Julian, Mr. Henry Forbes 0 0 113044 26.0000 E60 S
male 32.5000 32.50000 1 0 Keeping, Mr. Edwin 0 0 113503 211.5000 C132 C
male 58.0000 58.00000 1 0 Kent, Mr. Edward Austin 0 0 11771 29.7000 B37 C
male 41.0000 41.00000 1 0 Kenyon, Mr. Frederick R 1 0 17464 51.8625 D21 S
female NA 28.62425 1 1 Kenyon, Mrs. Frederick R (Marion) 1 0 17464 51.8625 D21 S
male 42.0000 42.00000 1 1 Kimball, Mr. Edwin Nelson Jr 1 0 11753 52.5542 D19 S
female 45.0000 45.00000 1 1 Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons) 1 0 11753 52.5542 D19 S
male NA 30.48562 1 0 Klaber, Mr. Herman 0 0 113028 26.5500 C124 S
female 39.0000 39.00000 1 1 Kreuchen, Miss. Emilie 0 0 24160 211.3375 NA S
female 49.0000 49.00000 1 1 Leader, Dr. Alice (Farnham) 0 0 17465 25.9292 D17 S
female 30.0000 30.00000 1 1 LeRoy, Miss. Bertha 0 0 PC 17761 106.4250 NA C
male 35.0000 35.00000 1 1 Lesurer, Mr. Gustave J 0 0 PC 17755 512.3292 B101 C
male NA 30.48562 1 0 Lewy, Mr. Ervin G 0 0 PC 17612 27.7208 NA C
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male NA 30.48562 3 0 Thomas, Mr. Tannous 0 0 2684 7.2250 NA C
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male NA 30.48562 3 0 Thomson, Mr. Alexander Morrison 0 0 32302 8.0500 NA S
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male 11.5000 11.50000 3 0 van Billiard, Master. Walter John 1 1 A/5. 851 14.5000 NA S
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female 10.0000 10.00000 3 0 Van Impe, Miss. Catharina 0 2 345773 24.1500 NA S
male 36.0000 36.00000 3 0 Van Impe, Mr. Jean Baptiste 1 1 345773 24.1500 NA S
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male 33.0000 33.00000 3 0 Vande Velde, Mr. Johannes Joseph 0 0 345780 9.5000 NA S
male 28.0000 28.00000 3 0 Vande Walle, Mr. Nestor Cyriel 0 0 345770 9.5000 NA S
male 28.0000 28.00000 3 0 Vanden Steen, Mr. Leo Peter 0 0 345783 9.5000 NA S
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male 22.0000 22.00000 3 0 Waelens, Mr. Achille 0 0 345767 9.0000 NA S
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male NA 30.48562 3 0 Warren, Mr. Charles William 0 0 C.A. 49867 7.5500 NA S
male NA 30.48562 3 0 Webber, Mr. James 0 0 SOTON/OQ 3101316 8.0500 NA S
male 32.5000 32.50000 3 0 Wenzel, Mr. Linhart 0 0 345775 9.5000 NA S
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5 Functions

A function is a bit of code which, when called, performs a task. It can take various inputs, called arguments, and return outputs.

Base R includes numerous built-in functions and there are thousands more R functions in packages available on CRAN and elsewhere.

We have previously used ready-made functions (from Base R or from packages), e.g. mutate(), help(),round(),clean_names(), mean().

We can also write your own functions, called “user-defined functions”.

5.1 Why we use functions

  • They make code shorter and easier to understand.

  • They make it easier to update code, as we only need to change one place.

  • Functions allow us to easily repeat similar tasks.

  • They help reduce errors caused from copying and pasting code multiple times.

Up until now we’ve been writing code line by line and running it sequentially. As effective as this has been, things can get messy as we scale up to larger problems.

We can avoid messy code by organising it into chunks of reusable code called functions. This allows us to create blocks of code dedicated to performing a specific procedure or task. We can then ‘call’ this function to perform its specific task as and when needed. Understanding why and how to write functions is an important part of writing code that is consistent, readable and reproducible.

In addition to this, we use functions to do everything in R - from making a vector to creating models and plots. Having more hands on experience with functions can give you a greater appreciation for how the functions, we heavily depend on, are implemented.

We can take one complicated operation and wrap it up inside a function and use it wherever we need.

For example, take the mean() function from base R that calculates the average value of a given vector — we could write out the average calculation by hand:

# Creating a vector
numbers <- 1:19

# Calculating the mean 
# Here I am using the sum function and the length function
mean_numbers <- sum(numbers) / length(numbers)

# To Display the data
mean_numbers
[1] 10
# Using the mean function
mean(numbers)
[1] 10

Nothing stops us from writing out sum(numbers) / length(numbers) every time we need to calculate an average.

Using the mean function is much easier.

Another example here is when we clean our column names, it is much easier to use the janitor::clean_names() function than to run the multiple lines of code using gsub()

5.2 Types of Functions

In R we have three types of functions:

  1. ‘Predefined’ Functions

    We have already used several of these, such as c(), round(), print(), etc. These functions, like the name suggests are built into R and are always available as we have seen by pressing tab in the console.

  2. ‘User-defined’ Functions

    These are functions created by us to carry out a specific task. Declared using function().

  3. Anonymous Functions

    Still user defined - generally one line functions used within a larger piece of code, they have name, but still do specific tasks.

In this section we’re going to focus on User Defined functions.

5.3 How to write a function

The basic structure of a function looks like this:

# Basic structure of a function

function_name <- function(input) { 
  
                 code_statements
  
                 return(output)
}

In R, we use a function to make a function!

We use function() to create a function and assign it to the object function_name.

In the same way we use the list() function to create and return a list object, the function() function will create and return a function object.

The code for the task you want your function to perform goes inside the curly brackets {}, and any objects you wish the function to work on go inside the parenthesis ().

The return() function will return whatever value is after it. When this is executed the function will stop at that line and return the given value.

No code after the return statement will be executed

Note that the keyword return is not necessary; but any function without a return statement will return the final variable called - this is functionally the same as using a return statement, but more implicit and therefore less clear. If we do not give a variable to be returned, or state one at the end of our function we will get no value returned from our function. This could be useful in some cases, for example if you just want to print out some results to the console, or display a graph, without returning any data.

5.4 Example of a basic function

# Here is a simple function to add together two values and return the sum.

sum_two_values <- function(value_1, value_2) { 
  
                  result <- value_1 + value_2
  
                  return(result)
}

Our new function sum_two_values() will exist in our global environment which can be seen in the panel on the right hand side.

5.5 Calling Functions

Calling user defined functions works the same way as calling the built-in functions we’ve used so far.

Simply, type the function name followed by parentheses containing any required arguments.

# I can now run my function

sum_two_values(value_1 = 1, value_2 = 2)
[1] 3
# Calling the function by itself prints the result to the console
# Alternatively the result can be saved as a new variable

result <- sum_two_values(value_1 = 3, value_2 = 4)

#To display the data

result
[1] 7

TIP - If you have written a chunk of code that you want to turn into a function, highlight the code chunk and press Ctrl + ALt + x on windows or control + option X on a Mac. A pop up will appear that will ask you to select a function name and the inputs and code structure needed to turn your code into a function will be added automatically.

5.6 Exercise: Basic Functions

  1. The range() in R, doesn’t show the difference between the highest value and the lowest value, but rather shows the highest value and lowest values, can you create a function called range_difference() which returns the difference between the highest value and the lowest value?
# Creating a vector

age_vector <- c(10, 23, 30, 50)

# Calling the range function on the vector

range(age_vector)
[1] 10 50
  1. Write a function which:

    Converts from Fahrenheit to degrees Celsius using the equation below.

\[C=\frac{5}{9}(F - 32)\]

Check it performs how you expect using the examples:

  • fahrenheit_to_degrees_celsius(32) returns:
    • [1] 0
  • fahrenheit_to_degrees_celsius(11) returns:
    • [1] -11.7
  • fahrenheit_to_degrees_celsius(81.3) returns:
    • [1] 27.4
# Creating my function

range_difference <-  function(input){
  
                     commands
  
                     return(result)
}

# Running the base R range function

range(input)

# Running my custom range function

my_range(input)
# Creating our function

fahrenheit_to_degrees_celsius <- function(input) {
  
                                 commands
  
                                 return(celsius)
}

# Calling our function

fahrenheit_to_degrees_celsius(32)
# Creating my function

range_difference <-  function(age_vector){
  
                     result <- max(age_vector) - min(age_vector) 
  
                     return(result)

}

# Running the base R range function

range(age_vector)
[1] 10 50
# Running my custom range function

range_difference(age_vector)
[1] 40
# Creating our function

fahrenheit_to_degrees_celsius <- function(fahrenheit) {
  
               celsius <- (5 / 9) * (fahrenheit - 32)
  
  return(celsius)
}

# Calling our function

fahrenheit_to_degrees_celsius(32)
[1] 0

5.7 Scope

It’s important to notice here that the variables I create within my functions are not available outside of my function.

In R storage is broken down into environments and then scopes. Most of the time you’ll be working in the global environment (right-hand panel).

In the global environment, there are two types of scopes, global and local. Variables that have global scope are visible everywhere - in every function, loop, conditional, basically every part of the code (after they have been declared). However, variables that have local scope are only visible inside their specific area and hence can only be used within that local scope.

When we create a new function we effectively create a new local scope – so the variables we declare within our functions are only available within that local scope.

Here is a classical example to demonstrate this.

# Creating a function

scope_function = function(){
  # this variable is local to the 
  # scope_function and cannot be  
  # accessed outside this function 
  weight_kg <- 60
  
} 
# To display the result
# This will not work as z does not exist outside the function

weight_kg

This will give the error message below,

Error: object ‘weight_kg’ not found

5.8 Arguments

In short, arguments are things you pass to a function, when you call it, to enable the function to perform it’s task.

5.8.1 Required Arguments

Like the name suggests, these are arguments which are required by a function and must be passed in the correct order.

# Creating our function

arithmetic <- function(a, b) {
  
              return ((a ^ b) / b)
}

# Calling our function

arithmetic(a = 4, b = 9)
[1] 29127.11

a and b are both required arguments. We would get a missing error if we did not pass both arguments.

5.8.2 Default arguments

This is where, in the function declaration, the value of the parameter is pre-set. The function will retain this value unless it is changed when the function is called.

# Creating our function

arithmetic <- function(a, b = 1) {
  
              return ((a ^ b) / b)
}

# Calling our function

arithmetic(a = 5)
[1] 5
# Calling our function overwriting the default value for b

arithmetic(a = 5, b = 2)
[1] 12.5

In the first example above the function will take a = 5 and use the default value b = 1. Whereas, in the second example, we overwrite b = 1 by passing the argument 2.

5.8.3 Dot-dot-dot arguments

This is also known as the ellipsis argument.

You will often see in list of arguments a function takes, in the documentation, this .... This is a special argument which captures any number of arguments that aren’t otherwise matched.

Sometimes being able to pass an arbitrary number of arguments can be useful, especially when another function is called within a wrapper function. This requires the ellipsis construct, …, which is designed to pass a variable number of arguments to a function.

Here’s an example: We created this function earlier.

# Creating our function
                                          # Added ...
fahrenheit_to_degrees_celsius <- function(fahrenheit, ...) {
  
  celsius <- (5 / 9) * (fahrenheit - 32)
  # Added round() with ...
  celsius <- round(celsius, ...)
  
  return(celsius)
}

We can use function as normally.

# Calling the function

fahrenheit_to_degrees_celsius(fahrenheit = 36)
[1] 2

We can also include a digits argument within this function which is coming from the round function.

# Calling the function

fahrenheit_to_degrees_celsius(fahrenheit = 36, digits = 4)
[1] 2.2222

6 Summary

You should now:

  • Understand the programming concept of Loops.
  • Utilise loops and understand their place in R.
  • Be able to use conditional statements: if, else and else if.
  • Be familiar with else_if() and case_when() from dplyr.
  • Know what functions are and why they are useful.
  • Be able to distinguish between a function’s parameters and arguments.
    • Including different types of arguments
  • Understand the idea of ‘scope’.
    • i.e. Global scope vs Local scope
  • Be able to write and apply user defined functions.
  • Be able to apply functions to vector and tibble objects.