Horrors of the Atlantic Ocean

Part 1

When I saw this data set, I was intrigued about the data set. When I opened it, I was shocked. How many lives have been snatched away, taken away, uprooted from their homes, and crossed the Atlantic into the Americas? All men and women with vast histories on this 32.5 MB files. How much documentation is there on the enslaved people from the vessel names, captains, crew members, number of enslaved people per ship, to deaths during their journeys to the flags of their respective countries? Unbelievable amounts of details! It reminds me of how the Nazis documented everything in their wrath of destruction in World War 2. Since I am very spiritual, I am fascinated by reincarnation. The concept of pain and suffering and wondered if the enslaved people ever got into heaven. Countless lives, countless eyes, and countless stories all ended with shattered dreams. As I write this, I can hear the ocean and the waves crashing onto the boats as they tilt. Captains and crew members whip the enslaved people as they paddle the boats. A generation from now a new set of enslaved people will be born soon after they will forget their history.

On October 12th, 1492, Christopher Columbus discovered the Americas. Twenty-Two years later, the first enslaved people set foot in the Americas. The first chart documents the beginning of American history. The Native Americans didn’t know what was coming on the horizon. Total destruction of Native Americans was given to them on Thanksgiving. The first plot tells you about the first that set foot in the Americas. Portugal/Brazil brought the most enslaved people. As America gained its independence, you started to see the increase of slaves traveling from American Vessels. I am really shocked about how many countries were involved in the slave trade and how Portugal/Brazil brought so many enslaved people across the Atlantic from the very beginning till the end. The Portuguese and Spain were the first to enslave and transport them to the Americas.

The second graph describes the countries that sponsored the ship and the number of enslaved people embarked. Each vessel had several enslaved people that were tightly packed into each other. The box plot shows how many enslaved people were transported per country. You can see the outliers of each country and then the density of how much they brought to the Americas.The third graph compares the year of arrival from each country by the number of enslaved people they brought to the Americas. Portugal/Brazilian vessels brought the most enslaved people into the Americas. Each color represents the number of enslaved people that arrived in the Americas. They are color-coded by the population size. For example, the USA brought in a far lower number of slaves compared to Great Britain and Portugal, and Brazil.

Overall, this project opened my eyes to the fact that humans are very flawed. From the first enslaved people in 1514 till the 1960s, the African diaspora from the Americas has suffered generational amounts of emotional suffering. With the help of data, we can only imagine what they have been going through. What we can do as world citizens is learn from our past mistakes. This project had an impact on my life. And I am glad I did it. It makes me realize how I am so fortunate to be blessed with what I have been given.

Loading Data

library(tidyverse)
library(dplyr)
library(tidyr)
library(plyr)
library(ggplot2)
library(treemap)
library(ggplot2)
library(plotly)


setwd ("C:\\Users\\asing\\Desktop\\data_science\\data_110\\week_3")
voyage <- read_csv("acrosstheatlantic.csv")

Checking the data

head(voyage)
## # A tibble: 6 × 72
##   `Captain's name`           `Crew deaths during voyage` Crew at first landing…¹
##   <chr>                                            <dbl>                   <dbl>
## 1 Renault, Jacques-Joseph-Fr                           4                      NA
## 2 Mouchel, J-Fr                                        1                      NA
## 3 Vieillard, Grégoire                                 NA                      NA
## 4 Donat, Thomas                                        8                      NA
## 5 Auvray, Luc-David                                   18                      NA
## 6 Guinel, Jean                                         1                      NA
## # ℹ abbreviated name: ¹​`Crew at first landing of captives`
## # ℹ 69 more variables: `Crew at voyage outset` <dbl>,
## #   `Date vessel departed with captives` <dttm>,
## #   `Date vessel departed for homeport` <dttm>,
## #   `Display in compact mode` <lgl>,
## #   `Date vessel arrived with captives` <dttm>,
## #   `First place where captives were landed` <lgl>, …
#glimpse(voyage)
#unique(voyage$`Captive deaths during crossing`)

Year of Arrival and Voyages per year

colnames(voyage)[13] = "Arrival_Year"
colnames(voyage)[23] = "Flag_Of_Vessel"
colnames(voyage)[19] = "Total_Disembarked"
colnames(voyage)[18] = "Total_Embarked"

voyage1<- voyage %>% 
  mutate(Arrival_Year = as.factor(Arrival_Year)) %>% 
  filter(!is.na(Arrival_Year)) %>% 
  group_by(Arrival_Year) %>% 
  tally()
voyage1
## # A tibble: 337 × 2
##    Arrival_Year     n
##    <fct>        <int>
##  1 1514             1
##  2 1516             1
##  3 1519             1
##  4 1520             1
##  5 1526             5
##  6 1527             1
##  7 1532             5
##  8 1533             1
##  9 1534             2
## 10 1535             2
## # ℹ 327 more rows

Visualization of Country of Origin

p1 <- voyage %>% 
  drop_na(Flag_Of_Vessel) %>% 
  ggplot(aes(Arrival_Year,Flag_Of_Vessel))+
  geom_point()
p1
## Warning: Removed 1 rows containing missing values (`geom_point()`).

THe Amount of Slaves Per Country

p2 <- voyage %>% 
  drop_na(Flag_Of_Vessel) %>% 
  ggplot(aes(x = Flag_Of_Vessel, y = Total_Embarked, fill = Flag_Of_Vessel)) +
  geom_boxplot(alpha = 0.3) +
  theme(legend.position = "none", 
        axis.text.x = element_text(face = "bold",
                                 size = 10, angle = 50, hjust= 1),
        axis.text.y = element_text(face="bold",
                                  size =10, angle=50))
p2
## Warning: Removed 1231 rows containing non-finite values (`stat_boxplot()`).

Treemap Of The Arrival Year, The Total Arrival Of The Slaves, and Which Country Brought Them To The America’s First

treemap(voyage,index = "Flag_Of_Vessel", vSize = "Arrival_Year",
          vColor = "Total_Disembarked", type = "manual", palette = "RdYlBu")

Horrors of the Atlantic Ocean

Part 2

Investigating the Vessels Names

I wanted to know how many times they traveled across the ocean. It shows how they have been involved from first carrying 100 slaves to over 700 slaves per ship towards the mid-1700s. Royal African Company, each slave was sold for about 6 English pounds. For example, if a ship carried 450 and reached the Americas, they were sold for 3465 dollars (1 pound = 1.28 dollars). Human life isn’t worth 6 pounds which equals 7.70 dollars. The chains and the shackles. Imagine being in the slave’s shoes. The slaves had names, families, children, dreams, and desires. They had ambition, they had language, they had cultures, they had love before becoming slaves. It all came crashing down, captured for 6 pounds, then transported onto foreign lands. Chained together, whipped, and beaten into submission.

colnames(voyage)[54] = "Vessel_Name"

vess_name<- voyage %>% 
  mutate(Vessel_Name = as.factor(Vessel_Name)) %>% 
  filter(!is.na(Vessel_Name)) %>% 
  group_by(Vessel_Name) %>% 
  tally(sort = T)
vess_name
## # A tibble: 9,456 × 2
##    Vessel_Name                           n
##    <fct>                             <int>
##  1 Mary                                254
##  2 Nancy                               197
##  3 NS do Rosario S Antônio e Almas     182
##  4 NS da Conceição S Antônio e Almas   175
##  5 Africa                              144
##  6 NS del Rosario                      130
##  7 Elizabeth                           124
##  8 S Antônio                           117
##  9 Sally                               113
## 10 Betsey                              108
## # ℹ 9,446 more rows

Who Owned the Most Ships In The Slave Trade?

The Database below shows they are 9,664 rows of different owners of slave ships. In the data set, there were names of vessel owners and who owned these ships. I wanted to know who owned the most ships. To my surprise, I was shocked. That amount of detail is given. Through the power of a Google search, I obtained information about these owners and their motivations, and who was being influenced. Kings, Queens, and the allure of endless riches were the culprit of the enslavement of humans. Human nature to conquer and want more. It is true when they say money is the root of all evil.

colnames(voyage)[44] = "Vessel_Owner"

Vessel_Owner<- voyage %>% 
  mutate(Vessel_Owner = as.factor(Vessel_Owner)) %>% 
  filter(!is.na(Vessel_Owner)) %>% 
  group_by(Vessel_Owner) %>% 
  tally(sort = T)
Vessel_Owner
## # A tibble: 9,664 × 2
##    Vessel_Owner                                n
##    <fct>                                   <int>
##  1 Royal African Company                     642
##  2 West-Indische Compagnie                   425
##  3 Companhia Geral do Grão Pará e Maranhão   175
##  4 Companhia Geral de Pernambuco e Paraíba   155
##  5 Compagnie des Indes                       121
##  6 Middelburgsche Commercie Compagnie        112
##  7 Company of Royal Adventurers              103
##  8 Compagnie du Sénégal                       90
##  9 James, William                             84
## 10 Laroche, James*                            83
## # ℹ 9,654 more rows

How many Captains that did repeated trips?

I wanted to know which captains made repeated trips. Captain John Smith had 39 trips. To sail across the Atlantic took two months. That means Captain John Smith took 78 trips back and forth to the new world. That is equivalent to 6 and a half years of sailing. Bringing the slaves across the Atlantic and sailing back from the Americas to capture more slaves. There is little information on the captains of these ships. I imagine they had some sort of childhood trauma from carrying slaves across the Atlantic. I can only speculate, but they were numb to the atrocities done to the slaves.

colnames(voyage)[1] = "Captain_Names"

Captain_Names <- voyage %>% 
  mutate(Captain_Names = as.factor(Captain_Names)) %>% 
  filter(!is.na(Captain_Names)) %>% 
  group_by(Captain_Names) %>% 
  tally(sort = T) %>% 
  top_n(10)
## Selecting by n
Captain_Names
## # A tibble: 13 × 2
##    Captain_Names                n
##    <fct>                    <int>
##  1 Smith, John                 39
##  2 Williams, William           27
##  3 Brown, William              22
##  4 Garcia, Francisco Correa    22
##  5 Simmons, John               22
##  6 Kendall, John               20
##  7 Molyneux, Thomas            20
##  8 Silva, José Leite da        20
##  9 Brown, John                 19
## 10 Brown, James                18
## 11 Moraes, José de             18
## 12 Sá, Manoel Gomes de         18
## 13 Silva, José Gonçalves da    18

Scatter Plot to visualize the amount of Total slaved being embarked per year.

You can see the trend of slave ships arriving in the Americas. The total embarked tremendously went up toward the end of the slave trade. I believe the colonists realized there would be more slaves than non-slaves and decided to halt the influx of slaves into America. The slave trade ended because of abolitionist pressure from their perspective countries.

ggplot(voyage, aes(Arrival_Year, Total_Embarked)) +
  geom_point()+
  geom_smooth()
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 1632 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1632 rows containing missing values (`geom_point()`).

  labs( )
## named list()
## attr(,"class")
## [1] "labels"

Interactive Scatter Plot of Countries Involved

These are the Countries that owned the vessels that were in invovled in the slave trade. You can see the corelation of the slave ships being

p3 <- ggplot(voyage, aes(Arrival_Year, Total_Embarked, color = Flag_Of_Vessel)) +
     geom_point(alpha = 0.2)+
      geom_smooth(method = "lm", se= FALSE)

ggplotly(p3)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1632 rows containing non-finite values (`stat_smooth()`).

Individual Countries

This graph shows you the linear regression of the individual countries. There are relationships of countries

ggplot(voyage, aes(Arrival_Year, Total_Embarked, color = Flag_Of_Vessel)) +
  geom_point(alpha = 0.1)+
  facet_wrap(~ Flag_Of_Vessel)+
  geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1632 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1632 rows containing missing values (`geom_point()`).

Line Graph of the Countries that were involved

Line graph of all the countries that were involved in the slave trade.

ggplot(voyage, aes(x= Arrival_Year, y = Total_Embarked, group = Flag_Of_Vessel)) +
  geom_line(aes(color= Flag_Of_Vessel))
## Warning: Removed 9 rows containing missing values (`geom_line()`).

Facet Graph of Individual Countries

You can see the Countries being represented individually. There is a clear sign that Portugal, from the very beginning since being next to Africa, influenced the number of slaves it captured throughout the years. Spain remained steady from the very beginning. The United States also had an increased intake of slave ships brought to America. Great Britain had an increase in slave voyages before it stopped. As America gained independence on July 4th, 1776, it revamped its slave voyages before slaves were abolished. Over 13 million slaves were transported into the Americas. They built the Americas.

ggplot(voyage, aes(Arrival_Year, Total_Embarked, color = Flag_Of_Vessel)) +
  geom_line(alpha = 0.2)+
  facet_wrap(~ Flag_Of_Vessel)+
  geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1632 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 9 rows containing missing values (`geom_line()`).
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

Analysis From The Data

The saying Follow the money coined in the 1970s meant when you follow the money, you tend to uncover hidden connections and motives. When you chase money, you see who, what, where, and how financial incentives influenced what built America. Every major city of the Americas was built on the backs of enslaved people. From chopping down trees to picking the cotton from your clothes, the car you drive on that road was once carved out by an enslaved person, and billions of years of their lives vanished.

A British company called The Royal African Company had the most slave voyages, 642 slave voyages across the Atlantic from its inception in 1660 till its dissolution in 1752. The Royal African Company was a private English company with a monopoly on the British slave trade along the west coast of Africa. They transported over 187,000 enslaved people to the British colonies in the Americas—enslaved people across the Atlantic. When you dig deeper, you find out that they had leases of voyages. It’s shocking to know that they were sophisticated in the 1700s that you could have a lease. Who had the rights to different ports, grabbing the enslaved people with their shackles and loading them up on a ship. Since its dissolution, a new company called Royal Adventures had over 103 voyages across the Atlantic.

Another company called West-Indische Compagnie which is called the Dutch West India Company, was founded on June 3rd, 1621. The traders disagreed with the monopolistic policies of the Dutch East India Company (VOC). Then for the next 171 years till January 1st, 1792, they had 425 voyages across the Atlantic. Dutch West India Company wanted to take away the competition and eliminate competition from the Spanish and Portuguese. With multiple wars between European countries, the Dutch West India Company splinted into several different companies.

What I find fascinating is that money rules the world. Money tampers with judgment. The get-rich fantasy remains the same in all ages. Everyone in every walk of life is trying to one-up each other. The competition to gain material wealth and the disregard for human lives to acquire that wealth is jaw-dropping. The investors that invested in these companies were all from affluent upbringings. The old money, they say, is haunted by exploitation and slavery. The cycle of trauma continues to this day. The wealthy man wants more power; the rich want wealth the poor want to become rich.

History shouldn’t be kept secret. Understanding History provides tools for modern-day society to learn from and navigate our decision-making. For example, when the supreme court strike down affirmative action they ever consider the amount of enslaved people being transported across the Atlantic? The billions of years and lives taken from the enslaved people? When african americans got the civil rights movement, laws were passed that put them in jail—which destroyed their nuclear families. Drugs were introduced on purpose to break down their communities. Who created this destruction? When you follow the money, new doors open, and new motivations are revealed. With each new deal, handshakes are confirmed a new flag on the vessel is being shipped. Economies of scale are being created, but lives of the slaves and the african dispora are lost forever.

Bibliography

“The Transatlantic Slave Trade | Equal Justice Initiative.” Equal Justice Initiative Reports, 22 Feb. 2023, eji.org/report/transatlantic-slave-trade/. Accessed 7 July 2023.
“Slave Ships - Encyclopedia Virginia.” Encyclopedia Virginia, 28 Jan. 2022, encyclopediavirginia.org/entries/slave-ships-and-the-middle-passage/. Accessed 7 July 2023.
“Trans-Atlantic Slave Trade - Database.” Slavevoyages.org, 2023, www.slavevoyages.org/voyage/database. Accessed 7 July 2023.
“Business Organization | Definition, Types, History, Roles and Responsibilities, & Facts | Britannica Money.” Encyclopædia Britannica, 2023, www.britannica.com/money/topic/business-organization. Accessed 7 July 2023.